Mitigating Data Loss in a Storage Network

ABSTRACT

A method for execution by a storage network starts by maintaining loading and data access rate information for a storage node and estimating a future data access rate for the storage node. The method continues by determining a probability level of potential future data loss, based on the estimated future data access rate and in response to a determination that the probability level of potential future data loss compares unfavorably to a maximum probability of data loss threshold level the method continues by facilitating migration of at least a portion of data stored on the storage node for temporary storage in another storage node of the storage network.

CROSS REFERENCE TO RELATED PATENTS

The present U.S. Utility Patent Application, which claims prioritypursuant to 35 U.S.C. § 120 as a continuation of U.S. UtilityApplication No. 17/451,917, entitled “Stand-By Storage Nodes In StorageNetwork”, filed Oct. 22, 2021, which is a continuation of U.S. UtilityApplication No. 16/049,731, entitled “Using Internal Sensors To DetectAdverse Interference And Take Defensive Actions,” filed Jul. 30, 2018,issued as U.S. Pat. No. 11,188,665 on Nov. 30, 2021, which is acontinuation-in-part (CIP) of U.S. Utility Pat. Application No.14/986,279, entitled “Storing Data In A Dispersed Storage Network,”filed Dec. 31, 2015, issued as U.S. Pat. No. 10,069,915 on Sep. 4, 2018,which claims priority pursuant to 35 U.S.C. § 119(e) to U.S. ProvisionalApplication No. 62/121,667, entitled “Selecting A Storage Pool Of ADispersed Storage Network,” filed Feb. 27, 2015, all of which are herebyincorporated herein by reference in their entirety and made part of thepresent U.S. Utility Patent Application for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT — NOTAPPLICABLE INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACTDISC — NOT APPLICABLE BACKGROUND OF THE INVENTION Technical Field of theInvention

This invention relates generally to computer networks and moreparticularly to dispersed storage of data and distributed taskprocessing of data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or online purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a distributedcomputing system in accordance with the present invention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a diagram of an example of a distributed storage and taskprocessing in accordance with the present invention;

FIG. 4 is a schematic block diagram of an embodiment of an outbounddistributed storage and/or task (DST) processing in accordance with thepresent invention;

FIG. 5 is a logic diagram of an example of a method for outbound DSTprocessing in accordance with the present invention;

FIG. 6 is a schematic block diagram of an embodiment of a dispersederror encoding in accordance with the present invention;

FIG. 7 is a diagram of an example of a segment processing of thedispersed error encoding in accordance with the present invention;

FIG. 8 is a diagram of an example of error encoding and slicingprocessing of the dispersed error encoding in accordance with thepresent invention;

FIG. 9 is a diagram of an example of grouping selection processing ofthe outbound DST processing in accordance with the present invention;

FIG. 10 is a diagram of an example of converting data into slice groupsin accordance with the present invention;

FIG. 11 is a schematic block diagram of an embodiment of a DST executionunit in accordance with the present invention;

FIG. 12 is a schematic block diagram of an example of operation of a DSTexecution unit in accordance with the present invention;

FIG. 13 is a schematic block diagram of an embodiment of an inbounddistributed storage and/or task (DST) processing in accordance with thepresent invention;

FIG. 14 is a logic diagram of an example of a method for inbound DSTprocessing in accordance with the present invention;

FIG. 15 is a diagram of an example of de-grouping selection processingof the inbound DST processing in accordance with the present invention;

FIG. 16 is a schematic block diagram of an embodiment of a dispersederror decoding in accordance with the present invention;

FIG. 17 is a diagram of an example of de-slicing and error decodingprocessing of the dispersed error decoding in accordance with thepresent invention;

FIG. 18 is a diagram of an example of de-segment processing of thedispersed error decoding in accordance with the present invention;

FIG. 19 is a diagram of an example of converting slice groups into datain accordance with the present invention;

FIG. 20 is a diagram of an example of a distributed storage within thedistributed computing system in accordance with the present invention;

FIG. 21 is a schematic block diagram of an example of operation ofoutbound distributed storage and/or task (DST) processing for storingdata in accordance with the present invention;

FIG. 22 is a schematic block diagram of an example of a dispersed errorencoding for the example of FIG. 21 in accordance with the presentinvention;

FIG. 23 is a diagram of an example of converting data into pillar slicegroups for storage in accordance with the present invention;

FIG. 24 is a schematic block diagram of an example of a storageoperation of a DST execution unit in accordance with the presentinvention;

FIG. 25 is a schematic block diagram of an example of operation ofinbound distributed storage and/or task (DST) processing for retrievingdispersed error encoded data in accordance with the present invention;

FIG. 26 is a schematic block diagram of an example of a dispersed errordecoding for the example of FIG. 25 in accordance with the presentinvention;

FIG. 27 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module storing a plurality ofdata and a plurality of task codes in accordance with the presentinvention;

FIG. 28 is a schematic block diagram of an example of the distributedcomputing system performing tasks on stored data in accordance with thepresent invention;

FIG. 29 is a schematic block diagram of an embodiment of a taskdistribution module facilitating the example of FIG. 28 in accordancewith the present invention;

FIG. 30 is a diagram of a specific example of the distributed computingsystem performing tasks on stored data in accordance with the presentinvention;

FIG. 31 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module storing data and taskcodes for the example of FIG. 30 in accordance with the presentinvention;

FIG. 32 is a diagram of an example of DST allocation information for theexample of FIG. 30 in accordance with the present invention;

FIGS. 33 - 38 are schematic block diagrams of the DSTN module performingthe example of FIG. 30 in accordance with the present invention;

FIG. 39 is a diagram of an example of combining result information intofinal results for the example of FIG. 30 in accordance with the presentinvention;

FIG. 40A is a schematic block diagram of an embodiment of adecentralized agreement module in accordance with the present invention;

FIG. 40B is a flowchart illustrating an example of selecting theresource in accordance with the present invention;

FIG. 40C is a schematic block diagram of an embodiment of a dispersedstorage network (DSN) in accordance with the present invention;

FIG. 40D is a flowchart illustrating an example of accessing a dispersedstorage network (DSN) memory in accordance with the present invention;

FIG. 41A is a schematic block diagram of another embodiment of adispersed storage network (DSN) in accordance with the presentinvention;

FIG. 41B is a flowchart illustrating an example of selecting a storagepool in accordance with the present invention;

FIG. 42A is a schematic block diagram of another embodiment of adispersed storage network (DSN) in accordance with the presentinvention;

FIG. 42B is a flowchart illustrating an example of selecting recoverystorage resources in accordance with the present invention;

FIG. 43A is a schematic block diagram of another embodiment of adispersed storage network (DSN) in accordance with the presentinvention;

FIG. 43B is a flowchart illustrating an example of selecting a dataintegrity maintenance process in accordance with the present invention;

FIG. 44A is a schematic block diagram of another embodiment of adispersed storage network (DSN) in accordance with the presentinvention;

FIG. 44B is a flowchart illustrating an example of improving a dataretrieval reliability level in accordance with the present invention;

FIG. 45A is a schematic block diagram of another embodiment of adispersed storage network (DSN) in accordance with the presentinvention;

FIG. 45B is a flowchart illustrating an example of utilizing storageresources in accordance with the present invention;

FIGS. 46A and 46B are schematic block diagrams of another embodiment ofa dispersed storage network (DSN) in accordance with the presentinvention;

FIG. 46C is a flowchart illustrating an example of storing data inaccordance with the present invention;

FIG. 47A is a schematic block diagram of another embodiment of adispersed storage network (DSN) in accordance with the presentinvention;

FIG. 47B is a flowchart illustrating an example of maintaining integrityof stored data in accordance with the present invention;

FIG. 48A is a schematic block diagram of another embodiment of adispersed storage network (DSN) in accordance with the presentinvention;

FIG. 48B is a flowchart illustrating an example of selecting an encodeddata slice rebuilding resource in accordance with the present invention;

FIG. 49A is a schematic block diagram of another embodiment of adispersed storage network (DSN) in accordance with the presentinvention; and

FIG. 49B is a flowchart illustrating an example of diminishing anunfavorable impact of an unauthorized access to a storage resource inaccordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a distributedcomputing system 10 that includes a user device 12 and/or a user device14, a distributed storage and/or task (DST) processing unit 16, adistributed storage and/or task network (DSTN) managing unit 18, a DSTintegrity processing unit 20, and a distributed storage and/or tasknetwork (DSTN) module 22. The components of the distributed computingsystem 10 are coupled via a network 24, which may include one or morewireless and/or wire lined communication systems; one or more privateintranet systems and/or public internet systems; and/or one or morelocal area networks (LAN) and/or wide area networks (WAN).

The DSTN module 22 includes a plurality of distributed storage and/ortask (DST) execution units 36 that may be located at geographicallydifferent sites (e.g., one in Chicago, one in Milwaukee, etc.). Each ofthe DST execution units is operable to store dispersed error encodeddata and/or to execute, in a distributed manner, one or more tasks ondata. The tasks may be a simple function (e.g., a mathematical function,a logic function, an identify function, a find function, a search enginefunction, a replace function, etc.), a complex function (e.g.,compression, human and/or computer language translation, text-to-voiceconversion, voice-to-text conversion, etc.), multiple simple and/orcomplex functions, one or more algorithms, one or more applications,etc.

Each of the user devices 12 - 14, the DST processing unit 16, the DSTNmanaging unit 18, and the DST integrity processing unit 20 include acomputing core 26 and may be a portable computing device and/or a fixedcomputing device. A portable computing device may be a social networkingdevice, a gaming device, a cell phone, a smart phone, a personal digitalassistant, a digital music player, a digital video player, a laptopcomputer, a handheld computer, a tablet, a video game controller, and/orany other portable device that includes a computing core. A fixedcomputing device may be a personal computer (PC), a computer server, acable set-top box, a satellite receiver, a television set, a printer, afax machine, home entertainment equipment, a video game console, and/orany type of home or office computing equipment. User device 12 and DSTprocessing unit 16 are configured to include a DST client module 34.

With respect to interfaces, each interface 30, 32, and 33 includessoftware and/or hardware to support one or more communication links viathe network 24 indirectly and/or directly. For example, interface 30supports a communication link (e.g., wired, wireless, direct, via a LAN,via the network 24, etc.) between user device 14 and the DST processingunit 16. As another example, interface 32 supports communication links(e.g., a wired connection, a wireless connection, a LAN connection,and/or any other type of connection to/from the network 24) between userdevice 12 and the DSTN module 22 and between the DST processing unit 16and the DSTN module 22. As yet another example, interface 33 supports acommunication link for each of the DSTN managing unit 18 and DSTintegrity processing unit 20 to the network 24.

The distributed computing system 10 is operable to support dispersedstorage (DS) error encoded data storage and retrieval, to supportdistributed task processing on received data, and/or to supportdistributed task processing on stored data. In general and with respectto DS error encoded data storage and retrieval, the distributedcomputing system 10 supports three primary operations: storagemanagement, data storage and retrieval (an example of which will bediscussed with reference to FIGS. 20-26 ), and data storage integrityverification. In accordance with these three primary functions, data canbe encoded, distributedly stored in physically different locations, andsubsequently retrieved in a reliable and secure manner. Such a system istolerant of a significant number of failures (e.g., up to a failurelevel, which may be greater than or equal to a pillar width minus adecode threshold minus one) that may result from individual storagedevice failures and/or network equipment failures without loss of dataand without the need for a redundant or backup copy. Further, the systemallows the data to be stored for an indefinite period of time withoutdata loss and does so in a secure manner (e.g., the system is veryresistant to attempts at hacking the data).

The second primary function (i.e., distributed data storage andretrieval) begins and ends with a user device 12 - 14. For instance, ifa second type of user device 14 has data 40 to store in the DSTN module22, it sends the data 40 to the DST processing unit 16 via its interface30. The interface 30 functions to mimic a conventional operating system(OS) file system interface (e.g., network file system (NFS), flash filesystem (FFS), disk file system (DFS), file transfer protocol (FTP),web-based distributed authoring and versioning (WebDAV), etc.) and/or ablock memory interface (e.g., small computer system interface (SCSI),internet small computer system interface (iSCSI), etc.). In addition,the interface 30 may attach a user identification code (ID) to the data40.

To support storage management, the DSTN managing unit 18 performs DSmanagement services. One such DS management service includes the DSTNmanaging unit 18 establishing distributed data storage parameters (e.g.,vault creation, distributed storage parameters, security parameters,billing information, user profile information, etc.) for a user device12-14 individually or as part of a group of user devices. For example,the DSTN managing unit 18 coordinates creation of a vault (e.g., avirtual memory block) within memory of the DSTN module 22 for a userdevice, a group of devices, or for public access and establishes pervault dispersed storage (DS) error encoding parameters for a vault. TheDSTN managing unit 18 may facilitate storage of DS error encodingparameters for each vault of a plurality of vaults by updating registryinformation for the distributed computing system 10. The facilitatingincludes storing updated registry information in one or more of the DSTNmodule 22, the user device 12, the DST processing unit 16, and the DSTintegrity processing unit 20.

The DS error encoding parameters (e.g., or dispersed storage errorcoding parameters) include data segmenting information (e.g., how manysegments data (e.g., a file, a group of files, a data block, etc.) isdivided into), segment security information (e.g., per segmentencryption, compression, integrity checksum, etc.), error codinginformation (e.g., pillar width, decode threshold, read threshold, writethreshold, etc.), slicing information (e.g., the number of encoded dataslices that will be created for each data segment); and slice securityinformation (e.g., per encoded data slice encryption, compression,integrity checksum, etc.).

The DSTN managing unit 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSTN module 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSTN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSTN managing unit 18 tracks the number of times a useraccesses a private vault and/or public vaults, which can be used togenerate a per-access billing information. In another instance, the DSTNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generate aper-data-amount billing information.

Another DS management service includes the DSTN managing unit 18performing network operations, network administration, and/or networkmaintenance. Network operations includes authenticating user dataallocation requests (e.g., read and/or write requests), managingcreation of vaults, establishing authentication credentials for userdevices, adding/deleting components (e.g., user devices, DST executionunits, and/or DST processing units) from the distributed computingsystem 10, and/or establishing authentication credentials for DSTexecution units 36. Network administration includes monitoring devicesand/or units for failures, maintaining vault information, determiningdevice and/or unit activation status, determining device and/or unitloading, and/or determining any other system level operation thataffects the performance level of the system 10. Network maintenanceincludes facilitating replacing, upgrading, repairing, and/or expandinga device and/or unit of the system 10.

To support data storage integrity verification within the distributedcomputing system 10, the DST integrity processing unit 20 performsrebuilding of ‘bad’ or missing encoded data slices. At a high level, theDST integrity processing unit 20 performs rebuilding by periodicallyattempting to retrieve/list encoded data slices, and/or slice names ofthe encoded data slices, from the DSTN module 22. For retrieved encodedslices, they are checked for errors due to data corruption, outdatedversion, etc. If a slice includes an error, it is flagged as a ‘bad’slice. For encoded data slices that were not received and/or not listed,they are flagged as missing slices. Bad and/or missing slices aresubsequently rebuilt using other retrieved encoded data slices that aredeemed to be good slices to produce rebuilt slices. The rebuilt slicesare stored in memory of the DSTN module 22. Note that the DST integrityprocessing unit 20 may be a separate unit as shown, it may be includedin the DSTN module 22, it may be included in the DST processing unit 16,and/or distributed among the DST execution units 36.

To support distributed task processing on received data, the distributedcomputing system 10 has two primary operations: DST (distributed storageand/or task processing) management and DST execution on received data(an example of which will be discussed with reference to FIGS. 3-19 ).With respect to the storage portion of the DST management, the DSTNmanaging unit 18 functions as previously described. With respect to thetasking processing of the DST management, the DSTN managing unit 18performs distributed task processing (DTP) management services. One suchDTP management service includes the DSTN managing unit 18 establishingDTP parameters (e.g., user-vault affiliation information, billinginformation, user-task information, etc.) for a user device 12-14individually or as part of a group of user devices.

Another DTP management service includes the DSTN managing unit 18performing DTP network operations, network administration (which isessentially the same as described above), and/or network maintenance(which is essentially the same as described above). Network operationsinclude, but are not limited to, authenticating user task processingrequests (e.g., valid request, valid user, etc.), authenticating resultsand/or partial results, establishing DTP authentication credentials foruser devices, adding/deleting components (e.g., user devices, DSTexecution units, and/or DST processing units) from the distributedcomputing system, and/or establishing DTP authentication credentials forDST execution units.

To support distributed task processing on stored data, the distributedcomputing system 10 has two primary operations: DST (distributed storageand/or task) management and DST execution on stored data. With respectto the DST execution on stored data, if the second type of user device14 has a task request 38 for execution by the DSTN module 22, it sendsthe task request 38 to the DST processing unit 16 via its interface 30.An example of DST execution on stored data will be discussed in greaterdetail with reference to FIGS. 27-39 . With respect to the DSTmanagement, it is substantially similar to the DST management to supportdistributed task processing on received data.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an IO interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSTN interface module 76.

The DSTN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). TheDSTN interface module 76 and/or the network interface module 70 mayfunction as the interface 30 of the user device 14 of FIG. 1 . Furthernote that the IO device interface module 62 and/or the memory interfacemodules may be collectively or individually referred to as IO ports.

FIG. 3 is a diagram of an example of the distributed computing systemperforming a distributed storage and task processing operation. Thedistributed computing system includes a DST (distributed storage and/ortask) client module 34 (which may be in user device 14 and/or in DSTprocessing unit 16 of FIG. 1 ), a network 24, a plurality of DSTexecution units 1-n that includes two or more DST execution units 36 ofFIG. 1 (which form at least a portion of DSTN module 22 of FIG. 1 ), aDST managing module (not shown), and a DST integrity verification module(not shown). The DST client module 34 includes an outbound DSTprocessing section 80 and an inbound DST processing section 82. Each ofthe DST execution units 1-n includes a controller 86, a processingmodule 84, memory 88, a DT (distributed task) execution module 90, and aDST client module 34.

In an example of operation, the DST client module 34 receives data 92and one or more tasks 94 to be performed upon the data 92. The data 92may be of any size and of any content, where, due to the size (e.g.,greater than a few Terabytes), the content (e.g., secure data, etc.),and/or task(s) (e.g., MIPS intensive), distributed processing of thetask(s) on the data is desired. For example, the data 92 may be one ormore digital books, a copy of a company’s emails, a large-scale Internetsearch, a video security file, one or more entertainment video files(e.g., television programs, movies, etc.), data files, and/or any otherlarge amount of data (e.g., greater than a few Terabytes).

Within the DST client module 34, the outbound DST processing section 80receives the data 92 and the task(s) 94. The outbound DST processingsection 80 processes the data 92 to produce slice groupings 96. As anexample of such processing, the outbound DST processing section 80partitions the data 92 into a plurality of data partitions. For eachdata partition, the outbound DST processing section 80 dispersed storage(DS) error encodes the data partition to produce encoded data slices andgroups the encoded data slices into a slice grouping 96. In addition,the outbound DST processing section 80 partitions the task 94 intopartial tasks 98, where the number of partial tasks 98 may correspond tothe number of slice groupings 96.

The outbound DST processing section 80 then sends, via the network 24,the slice groupings 96 and the partial tasks 98 to the DST executionunits 1-n of the DSTN module 22 of FIG. 1 . For example, the outboundDST processing section 80 sends slice group 1 and partial task 1 to DSTexecution unit 1. As another example, the outbound DST processingsection 80 sends slice group #n and partial task #n to DST executionunit #n.

Each DST execution unit performs its partial task 98 upon its slicegroup 96 to produce partial results 102. For example, DST execution unit#1 performs partial task #1 on slice group #1 to produce a partialresult #1, for results. As a more specific example, slice group #1corresponds to a data partition of a series of digital books and thepartial task #1 corresponds to searching for specific phrases, recordingwhere the phrase is found, and establishing a phrase count. In this morespecific example, the partial result #1 includes information as to wherethe phrase was found and includes the phrase count.

Upon completion of generating their respective partial results 102, theDST execution units send, via the network 24, their partial results 102to the inbound DST processing section 82 of the DST client module 34.The inbound DST processing section 82 processes the received partialresults 102 to produce a result 104. Continuing with the specificexample of the preceding paragraph, the inbound DST processing section82 combines the phrase count from each of the DST execution units 36 toproduce a total phrase count. In addition, the inbound DST processingsection 82 combines the ‘where the phrase was found′ information fromeach of the DST execution units 36 within their respective datapartitions to produce ′where the phrase was found′ information for theseries of digital books.

In another example of operation, the DST client module 34 requestsretrieval of stored data within the memory of the DST execution units 36(e.g., memory of the DSTN module). In this example, the task 94 isretrieve data stored in the memory of the DSTN module. Accordingly, theoutbound DST processing section 80 converts the task 94 into a pluralityof partial tasks 98 and sends the partial tasks 98 to the respective DSTexecution units 1-n.

In response to the partial task 98 of retrieving stored data, a DSTexecution unit 36 identifies the corresponding encoded data slices 100and retrieves them. For example, DST execution unit #1 receives partialtask #1 and retrieves, in response thereto, retrieved slices #1. The DSTexecution units 36 send their respective retrieved slices 100 to theinbound DST processing section 82 via the network 24.

The inbound DST processing section 82 converts the retrieved slices 100into data 92. For example, the inbound DST processing section 82de-groups the retrieved slices 100 to produce encoded slices per datapartition. The inbound DST processing section 82 then DS error decodesthe encoded slices per data partition to produce data partitions. Theinbound DST processing section 82 de-partitions the data partitions torecapture the data 92.

FIG. 4 is a schematic block diagram of an embodiment of an outbounddistributed storage and/or task (DST) processing section 80 of a DSTclient module 34 FIG. 1 coupled to a DSTN module 22 of a FIG. 1 (e.g., aplurality of n DST execution units 36) via a network 24. The outboundDST processing section 80 includes a data partitioning module 110, adispersed storage (DS) error encoding module 112, a grouping selectormodule 114, a control module 116, and a distributed task control module118.

In an example of operation, the data partitioning module 110 partitionsdata 92 into a plurality of data partitions 120. The number ofpartitions and the size of the partitions may be selected by the controlmodule 116 via control 160 based on the data 92 (e.g., its size, itscontent, etc.), a corresponding task 94 to be performed (e.g., simple,complex, single step, multiple steps, etc.), DS encoding parameters(e.g., pillar width, decode threshold, write threshold, segment securityparameters, slice security parameters, etc.), capabilities of the DSTexecution units 36 (e.g., processing resources, availability ofprocessing recourses, etc.), and/or as may be inputted by a user, systemadministrator, or other operator (human or automated). For example, thedata partitioning module 110 partitions the data 92 (e.g., 100Terabytes) into 100,000 data segments, each being 1 Gigabyte in size.Alternatively, the data partitioning module 110 partitions the data 92into a plurality of data segments, where some of data segments are of adifferent size, are of the same size, or a combination thereof.

The DS error encoding module 112 receives the data partitions 120 in aserial manner, a parallel manner, and/or a combination thereof. For eachdata partition 120, the DS error encoding module 112 DS error encodesthe data partition 120 in accordance with control information 160 fromthe control module 116 to produce encoded data slices 122. The DS errorencoding includes segmenting the data partition into data segments,segment security processing (e.g., encryption, compression,watermarking, integrity check (e.g., CRC), etc.), error encoding,slicing, and/or per slice security processing (e.g., encryption,compression, watermarking, integrity check (e.g., CRC), etc.). Thecontrol information 160 indicates which steps of the DS error encodingare active for a given data partition and, for active steps, indicatesthe parameters for the step. For example, the control information 160indicates that the error encoding is active and includes error encodingparameters (e.g., pillar width, decode threshold, write threshold, readthreshold, type of error encoding, etc.).

The grouping selector module 114 groups the encoded slices 122 of a datapartition into a set of slice groupings 96. The number of slicegroupings corresponds to the number of DST execution units 36 identifiedfor a particular task 94. For example, if five DST execution units 36are identified for the particular task 94, the grouping selector modulegroups the encoded slices 122 of a data partition into five slicegroupings 96. The grouping selector module 114 outputs the slicegroupings 96 to the corresponding DST execution units 36 via the network24.

The distributed task control module 118 receives the task 94 andconverts the task 94 into a set of partial tasks 98. For example, thedistributed task control module 118 receives a task to find where in thedata (e.g., a series of books) a phrase occurs and a total count of thephrase usage in the data. In this example, the distributed task controlmodule 118 replicates the task 94 for each DST execution unit 36 toproduce the partial tasks 98. In another example, the distributed taskcontrol module 118 receives a task to find where in the data a firstphrase occurs, where in the data a second phrase occurs, and a totalcount for each phrase usage in the data. In this example, thedistributed task control module 118 generates a first set of partialtasks 98 for finding and counting the first phase and a second set ofpartial tasks for finding and counting the second phrase. Thedistributed task control module 118 sends respective first and/or secondpartial tasks 98 to each DST execution unit 36.

FIG. 5 is a logic diagram of an example of a method for outbounddistributed storage and task (DST) processing that begins at step 126where a DST client module receives data and one or more correspondingtasks. The method continues at step 128 where the DST client moduledetermines a number of DST units to support the task for one or moredata partitions. For example, the DST client module may determine thenumber of DST units to support the task based on the size of the data,the requested task, the content of the data, a predetermined number(e.g., user indicated, system administrator determined, etc.), availableDST units, capability of the DST units, and/or any other factorregarding distributed task processing of the data. The DST client modulemay select the same DST units for each data partition, may selectdifferent DST units for the data partitions, or a combination thereof.

The method continues at step 130 where the DST client module determinesprocessing parameters of the data based on the number of DST unitsselected for distributed task processing. The processing parametersinclude data partitioning information, DS encoding parameters, and/orslice grouping information. The data partitioning information includes anumber of data partitions, size of each data partition, and/ororganization of the data partitions (e.g., number of data blocks in apartition, the size of the data blocks, and arrangement of the datablocks). The DS encoding parameters include segmenting information,segment security information, error encoding information (e.g.,dispersed storage error encoding function parameters including one ormore of pillar width, decode threshold, write threshold, read threshold,generator matrix), slicing information, and/or per slice securityinformation. The slice grouping information includes informationregarding how to arrange the encoded data slices into groups for theselected DST units. As a specific example, if the DST client moduledetermines that five DST units are needed to support the task, then itdetermines that the error encoding parameters include a pillar width offive and a decode threshold of three.

The method continues at step 132 where the DST client module determinestask partitioning information (e.g., how to partition the tasks) basedon the selected DST units and data processing parameters. The dataprocessing parameters include the processing parameters and DST unitcapability information. The DST unit capability information includes thenumber of DT (distributed task) execution units, execution capabilitiesof each DT execution unit (e.g., MIPS capabilities, processing resources(e.g., quantity and capability of microprocessors, CPUs, digital signalprocessors, co-processor, microcontrollers, arithmetic logic circuitry,and/or and the other analog and/or digital processing circuitry),availability of the processing resources, memory information (e.g.,type, size, availability, etc.)), and/or any information germane toexecuting one or more tasks.

The method continues at step 134 where the DST client module processesthe data in accordance with the processing parameters to produce slicegroupings. The method continues at step 136 where the DST client modulepartitions the task based on the task partitioning information toproduce a set of partial tasks. The method continues at step 138 wherethe DST client module sends the slice groupings and the correspondingpartial tasks to respective DST units.

FIG. 6 is a schematic block diagram of an embodiment of the dispersedstorage (DS) error encoding module 112 of an outbound distributedstorage and task (DST) processing section. The DS error encoding module112 includes a segment processing module 142, a segment securityprocessing module 144, an error encoding module 146, a slicing module148, and a per slice security processing module 150. Each of thesemodules is coupled to a control module 116 to receive controlinformation 160 therefrom.

In an example of operation, the segment processing module 142 receives adata partition 120 from a data partitioning module and receivessegmenting information as the control information 160 from the controlmodule 116. The segmenting information indicates how the segmentprocessing module 142 is to segment the data partition 120. For example,the segmenting information indicates how many rows to segment the databased on a decode threshold of an error encoding scheme, indicates howmany columns to segment the data into based on a number and size of datablocks within the data partition 120, and indicates how many columns toinclude in a data segment 152. The segment processing module 142segments the data 120 into data segments 152 in accordance with thesegmenting information.

The segment security processing module 144, when enabled by the controlmodule 116, secures the data segments 152 based on segment securityinformation received as control information 160 from the control module116. The segment security information includes data compression,encryption, watermarking, integrity check (e.g., cyclic redundancy check(CRC), etc.), and/or any other type of digital security. For example,when the segment security processing module 144 is enabled, it maycompress a data segment 152, encrypt the compressed data segment, andgenerate a CRC value for the encrypted data segment to produce a securedata segment 154. When the segment security processing module 144 is notenabled, it passes the data segments 152 to the error encoding module146 or is bypassed such that the data segments 152 are provided to theerror encoding module 146.

The error encoding module 146 encodes the secure data segments 154 inaccordance with error correction encoding parameters received as controlinformation 160 from the control module 116. The error correctionencoding parameters (e.g., also referred to as dispersed storage errorcoding parameters) include identifying an error correction encodingscheme (e.g., forward error correction algorithm, a Reed-Solomon basedalgorithm, an online coding algorithm, an information dispersalalgorithm, etc.), a pillar width, a decode threshold, a read threshold,a write threshold, etc. For example, the error correction encodingparameters identify a specific error correction encoding scheme,specifies a pillar width of five, and specifies a decode threshold ofthree. From these parameters, the error encoding module 146 encodes adata segment 154 to produce an encoded data segment 156.

The slicing module 148 slices the encoded data segment 156 in accordancewith the pillar width of the error correction encoding parametersreceived as control information 160. For example, if the pillar width isfive, the slicing module 148 slices an encoded data segment 156 into aset of five encoded data slices. As such, for a plurality of encodeddata segments 156 for a given data partition, the slicing module outputsa plurality of sets of encoded data slices 158.

The per slice security processing module 150, when enabled by thecontrol module 116, secures each encoded data slice 158 based on slicesecurity information received as control information 160 from thecontrol module 116. The slice security information includes datacompression, encryption, watermarking, integrity check (e.g., CRC,etc.), and/or any other type of digital security. For example, when theper slice security processing module 150 is enabled, it compresses anencoded data slice 158, encrypts the compressed encoded data slice, andgenerates a CRC value for the encrypted encoded data slice to produce asecure encoded data slice 122. When the per slice security processingmodule 150 is not enabled, it passes the encoded data slices 158 or isbypassed such that the encoded data slices 158 are the output of the DSerror encoding module 112. Note that the control module 116 may beomitted and each module stores its own parameters.

FIG. 7 is a diagram of an example of a segment processing of a dispersedstorage (DS) error encoding module. In this example, a segmentprocessing module 142 receives a data partition 120 that includes 45data blocks (e.g., d 1 - d 45), receives segmenting information (i.e.,control information 160) from a control module, and segments the datapartition 120 in accordance with the control information 160 to producedata segments 152. Each data block may be of the same size as other datablocks or of a different size. In addition, the size of each data blockmay be a few bytes to megabytes of data. As previously mentioned, thesegmenting information indicates how many rows to segment the datapartition into, indicates how many columns to segment the data partitioninto, and indicates how many columns to include in a data segment.

In this example, the decode threshold of the error encoding scheme isthree; as such the number of rows to divide the data partition into isthree. The number of columns for each row is set to 15, which is basedon the number and size of data blocks. The data blocks of the datapartition are arranged in rows and columns in a sequential order (i.e.,the first row includes the first 15 data blocks; the second row includesthe second 15 data blocks; and the third row includes the last 15 datablocks).

With the data blocks arranged into the desired sequential order, theyare divided into data segments based on the segmenting information. Inthis example, the data partition is divided into 8 data segments; thefirst 7 include 2 columns of three rows and the last includes 1 columnof three rows. Note that the first row of the 8 data segments is insequential order of the first 15 data blocks; the second row of the 8data segments in sequential order of the second 15 data blocks; and thethird row of the 8 data segments in sequential order of the last 15 datablocks. Note that the number of data blocks, the grouping of the datablocks into segments, and size of the data blocks may vary toaccommodate the desired distributed task processing function.

FIG. 8 is a diagram of an example of error encoding and slicingprocessing of the dispersed error encoding processing the data segmentsof FIG. 7 . In this example, data segment 1 includes 3 rows with eachrow being treated as one word for encoding. As such, data segment 1includes three words for encoding: word 1 including data blocks d 1 andd 2, word 2 including data blocks d 16 and d 17, and word 3 includingdata blocks d 31 and d 32. Each of data segments 2 - 7 includes threewords where each word includes two data blocks. Data segment 8 includesthree words where each word includes a single data block (e.g., d 15, d30, and d 45).

In operation, an error encoding module 146 and a slicing module 148convert each data segment into a set of encoded data slices inaccordance with error correction encoding parameters as controlinformation 160. More specifically, when the error correction encodingparameters indicate a unity matrix Reed-Solomon based encodingalgorithm, 5 pillars, and decode threshold of 3, the first three encodeddata slices of the set of encoded data slices for a data segment aresubstantially similar to the corresponding word of the data segment. Forinstance, when the unity matrix Reed-Solomon based encoding algorithm isapplied to data segment 1, the content of the first encoded data slice(DS1_d 1&2) of the first set of encoded data slices (e.g., correspondingto data segment 1) is substantially similar to content of the first word(e.g., d 1 & d 2); the content of the second encoded data slice (DS1_d16&17) of the first set of encoded data slices is substantially similarto content of the second word (e.g., d 16 & d 17); and the content ofthe third encoded data slice (DS1_d 31&32) of the first set of encodeddata slices is substantially similar to content of the third word (e.g.,d 31 & d 32).

The content of the fourth and fifth encoded data slices (e.g., ES1_1 andES1_2) of the first set of encoded data slices include error correctiondata based on the first — third words of the first data segment. Withsuch an encoding and slicing scheme, retrieving any three of the fiveencoded data slices allows the data segment to be accuratelyreconstructed.

The encoding and slices of data segments 2 - 7 yield sets of encodeddata slices similar to the set of encoded data slices of data segment 1.For instance, the content of the first encoded data slice (DS2_d3&4) ofthe second set of encoded data slices (e.g., corresponding to datasegment 2) is substantially similar to content of the first word (e.g.,d3 & d4); the content of the second encoded data slice (DS2_d18&19) ofthe second set of encoded data slices is substantially similar tocontent of the second word (e.g., d18 & d19); and the content of thethird encoded data slice (DS2_d33&34) of the second set of encoded dataslices is substantially similar to content of the third word (e.g., d33& d34). The content of the fourth and fifth encoded data slices (e.g.,ES1_1 and ES1_2) of the second set of encoded data slices includes errorcorrection data based on the first — third words of the second datasegment.

FIG. 9 is a diagram of an example of grouping selection processing of anoutbound distributed storage and task (DST) processing in accordancewith grouping selector information as control information 160 from acontrol module. Encoded slices for data partition 122 are grouped inaccordance with the control information 160 to produce slice groupings96. In this example, a grouping selector module 114 organizes theencoded data slices into five slice groupings (e.g., one for each DSTexecution unit of a distributed storage and task network (DSTN) module).As a specific example, the grouping selector module 114 creates a firstslice grouping for a DST execution unit #1, which includes first encodedslices of each of the sets of encoded slices. As such, the first DSTexecution unit receives encoded data slices corresponding to data blocks1-15 (e.g., encoded data slices of contiguous data).

The grouping selector module 114 also creates a second slice groupingfor a DST execution unit #2, which includes second encoded slices ofeach of the sets of encoded slices. As such, the second DST executionunit receives encoded data slices corresponding to data blocks 16-30.The grouping selector module 114 further creates a third slice groupingfor DST execution unit #3, which includes third encoded slices of eachof the sets of encoded slices. As such, the third DST execution unitreceives encoded data slices corresponding to data blocks 31-45.

The grouping selector module 114 creates a fourth slice grouping for DSTexecution unit #4, which includes fourth encoded slices of each of thesets of encoded slices. As such, the fourth DST execution unit receivesencoded data slices corresponding to first error encoding information(e.g., encoded data slices of error coding (EC) data). The groupingselector module 114 further creates a fifth slice grouping for DSTexecution unit #5, which includes fifth encoded slices of each of thesets of encoded slices. As such, the fifth DST execution unit receivesencoded data slices corresponding to second error encoding information.

FIG. 10 is a diagram of an example of converting data 92 into slicegroups that expands on the preceding figures. As shown, the data 92 ispartitioned in accordance with a partitioning function 164 into aplurality of data partitions (1-x, where x is an integer greater than4). Each data partition (or chunk set of data) is encoded and groupedinto slice groupings as previously discussed by an encoding and groupingfunction 166. For a given data partition, the slice groupings are sentto distributed storage and task (DST) execution units. From datapartition to data partition, the ordering of the slice groupings to theDST execution units may vary.

For example, the slice groupings of data partition #1 is sent to the DSTexecution units such that the first DST execution receives first encodeddata slices of each of the sets of encoded data slices, whichcorresponds to a first continuous data chunk of the first data partition(e.g., refer to FIG. 9 ), a second DST execution receives second encodeddata slices of each of the sets of encoded data slices, whichcorresponds to a second continuous data chunk of the first datapartition, etc.

For the second data partition, the slice groupings may be sent to theDST execution units in a different order than it was done for the firstdata partition. For instance, the first slice grouping of the seconddata partition (e.g., slice group 2_1) is sent to the second DSTexecution unit; the second slice grouping of the second data partition(e.g., slice group 2_2) is sent to the third DST execution unit; thethird slice grouping of the second data partition (e.g., slice group2_3) is sent to the fourth DST execution unit; the fourth slice groupingof the second data partition (e.g., slice group 2_4, which includesfirst error coding information) is sent to the fifth DST execution unit;and the fifth slice grouping of the second data partition (e.g., slicegroup 2_5, which includes second error coding information) is sent tothe first DST execution unit.

The pattern of sending the slice groupings to the set of DST executionunits may vary in a predicted pattern, a random pattern, and/or acombination thereof from data partition to data partition. In addition,from data partition to data partition, the set of DST execution unitsmay change. For example, for the first data partition, DST executionunits 1-5 may be used; for the second data partition, DST executionunits 6-10 may be used; for the third data partition, DST executionunits 3-7 may be used; etc. As is also shown, the task is divided intopartial tasks that are sent to the DST execution units in conjunctionwith the slice groupings of the data partitions.

FIG. 11 is a schematic block diagram of an embodiment of a DST(distributed storage and/or task) execution unit that includes aninterface 169, a controller 86, memory 88, one or more DT (distributedtask) execution modules 90, and a DST client module 34. The memory 88 isof sufficient size to store a significant number of encoded data slices(e.g., thousands of slices to hundreds-of-millions of slices) and mayinclude one or more hard drives and/or one or more solid-state memorydevices (e.g., flash memory, DRAM, etc.).

In an example of storing a slice group, the DST execution modulereceives a slice grouping 96 (e.g., slice group #1) via interface 169.The slice grouping 96 includes, per partition, encoded data slices ofcontiguous data or encoded data slices of error coding (EC) data. Forslice group #1, the DST execution module receives encoded data slices ofcontiguous data for partitions #1 and #x (and potentially others between3 and x) and receives encoded data slices of EC data for partitions #2and #3 (and potentially others between 3 and x). Examples of encodeddata slices of contiguous data and encoded data slices of error coding(EC) data are discussed with reference to FIG. 9 . The memory 88 storesthe encoded data slices of slice groupings 96 in accordance with memorycontrol information 174 it receives from the controller 86.

The controller 86 (e.g., a processing module, a CPU, etc.) generates thememory control information 174 based on a partial task(s) 98 anddistributed computing information (e.g., user information (e.g., userID, distributed computing permissions, data access permission, etc.),vault information (e.g., virtual memory assigned to user, user group,temporary storage for task processing, etc.), task validationinformation, etc.). For example, the controller 86 interprets thepartial task(s) 98 in light of the distributed computing information todetermine whether a requestor is authorized to perform the task 98, isauthorized to access the data, and/or is authorized to perform the taskon this particular data. When the requestor is authorized, thecontroller 86 determines, based on the task 98 and/or another input,whether the encoded data slices of the slice grouping 96 are to betemporarily stored or permanently stored. Based on the foregoing, thecontroller 86 generates the memory control information 174 to write theencoded data slices of the slice grouping 96 into the memory 88 and toindicate whether the slice grouping 96 is permanently stored ortemporarily stored.

With the slice grouping 96 stored in the memory 88, the controller 86facilitates execution of the partial task(s) 98. In an example, thecontroller 86 interprets the partial task 98 in light of thecapabilities of the DT execution module(s) 90. The capabilities includeone or more of MIPS capabilities, processing resources (e.g., quantityand capability of microprocessors, CPUs, digital signal processors,co-processor, microcontrollers, arithmetic logic circuitry, and/or anyother analog and/or digital processing circuitry), availability of theprocessing resources, etc. If the controller 86 determines that the DTexecution module(s) 90 have sufficient capabilities, it generates taskcontrol information 176.

The task control information 176 may be a generic instruction (e.g.,perform the task on the stored slice grouping) or a series ofoperational codes. In the former instance, the DT execution module 90includes a co-processor function specifically configured (fixed orprogrammed) to perform the desired task 98. In the latter instance, theDT execution module 90 includes a general processor topology where thecontroller stores an algorithm corresponding to the particular task 98.In this instance, the controller 86 provides the operational codes(e.g., assembly language, source code of a programming language, objectcode, etc.) of the algorithm to the DT execution module 90 forexecution.

Depending on the nature of the task 98, the DT execution module 90 maygenerate intermediate partial results 102 that are stored in the memory88 or in a cache memory (not shown) within the DT execution module 90.In either case, when the DT execution module 90 completes execution ofthe partial task 98, it outputs one or more partial results 102. Thepartial results 102 may also be stored in memory 88.

If, when the controller 86 is interpreting whether capabilities of theDT execution module(s) 90 can support the partial task 98, thecontroller 86 determines that the DT execution module(s) 90 cannotadequately support the task 98 (e.g., does not have the right resources,does not have sufficient available resources, available resources wouldbe too slow, etc.), it then determines whether the partial task 98should be fully offloaded or partially offloaded.

If the controller 86 determines that the partial task 98 should be fullyoffloaded, it generates DST control information 178 and provides it tothe DST client module 34. The DST control information 178 includes thepartial task 98, memory storage information regarding the slice grouping96, and distribution instructions. The distribution instructionsinstruct the DST client module 34 to divide the partial task 98 intosub-partial tasks 172, to divide the slice grouping 96 into sub-slicegroupings 170, and identify other DST execution units. The DST clientmodule 34 functions in a similar manner as the DST client module 34 ofFIGS. 3-10 to produce the sub-partial tasks 172 and the sub-slicegroupings 170 in accordance with the distribution instructions.

The DST client module 34 receives DST feedback 168 (e.g., sub-partialresults), via the interface 169, from the DST execution units to whichthe task was offloaded. The DST client module 34 provides thesub-partial results to the DST execution unit, which processes thesub-partial results to produce the partial result(s) 102.

If the controller 86 determines that the partial task 98 should bepartially offloaded, it determines what portion of the task 98 and/orslice grouping 96 should be processed locally and what should beoffloaded. For the portion that is being locally processed, thecontroller 86 generates task control information 176 as previouslydiscussed. For the portion that is being offloaded, the controller 86generates DST control information 178 as previously discussed.

When the DST client module 34 receives DST feedback 168 (e.g.,sub-partial results) from the DST executions units to which a portion ofthe task was offloaded, it provides the sub-partial results to the DTexecution module 90. The DT execution module 90 processes thesub-partial results with the sub-partial results it created to producethe partial result(s) 102.

The memory 88 may be further utilized to retrieve one or more of storedslices 100, stored results 104, partial results 102 when the DTexecution module 90 stores partial results 102 and/or results 104 in thememory 88. For example, when the partial task 98 includes a retrievalrequest, the controller 86 outputs the memory control 174 to the memory88 to facilitate retrieval of slices 100 and/or results 104.

FIG. 12 is a schematic block diagram of an example of operation of adistributed storage and task (DST) execution unit storing encoded dataslices and executing a task thereon. To store the encoded data slices ofa partition 1 of slice grouping 1, a controller 86 generates writecommands as memory control information 174 such that the encoded slicesare stored in desired locations (e.g., permanent or temporary) withinmemory 88.

Once the encoded slices are stored, the controller 86 provides taskcontrol information 176 to a distributed task (DT) execution module 90.As a first step of executing the task in accordance with the taskcontrol information 176, the DT execution module 90 retrieves theencoded slices from memory 88. The DT execution module 90 thenreconstructs contiguous data blocks of a data partition. As shown forthis example, reconstructed contiguous data blocks of data partition 1include data blocks 1-15 (e.g., d 1- d 15).

With the contiguous data blocks reconstructed, the DT execution module90 performs the task on the reconstructed contiguous data blocks. Forexample, the task may be to search the reconstructed contiguous datablocks for a particular word or phrase, identify where in thereconstructed contiguous data blocks the particular word or phraseoccurred, and/or count the occurrences of the particular word or phraseon the reconstructed contiguous data blocks. The DST execution unitcontinues in a similar manner for the encoded data slices of otherpartitions in slice grouping 1. Note that with using the unity matrixerror encoding scheme previously discussed, if the encoded data slicesof contiguous data are uncorrupted, the decoding of them is a relativelystraightforward process of extracting the data.

If, however, an encoded data slice of contiguous data is corrupted (ormissing), it can be rebuilt by accessing other DST execution units thatare storing the other encoded data slices of the set of encoded dataslices of the corrupted encoded data slice. In this instance, the DSTexecution unit having the corrupted encoded data slices retrieves atleast three encoded data slices (of contiguous data and of error codingdata) in the set from the other DST execution units (recall for thisexample, the pillar width is 5 and the decode threshold is 3). The DSTexecution unit decodes the retrieved data slices using the DS errorencoding parameters to recapture the corresponding data segment. The DSTexecution unit then re-encodes the data segment using the DS errorencoding parameters to rebuild the corrupted encoded data slice. Oncethe encoded data slice is rebuilt, the DST execution unit functions aspreviously described.

FIG. 13 is a schematic block diagram of an embodiment of an inbounddistributed storage and/or task (DST) processing section 82 of a DSTclient module coupled to DST execution units of a distributed storageand task network (DSTN) module via a network 24. The inbound DSTprocessing section 82 includes a de-grouping module 180, a DS (dispersedstorage) error decoding module 182, a data de-partitioning module 184, acontrol module 186, and a distributed task control module 188. Note thatthe control module 186 and/or the distributed task control module 188may be separate modules from corresponding ones of outbound DSTprocessing section or may be the same modules.

In an example of operation, the DST execution units have completedexecution of corresponding partial tasks on the corresponding slicegroupings to produce partial results 102. The inbound DST processingsection 82 receives the partial results 102 via the distributed taskcontrol module 188. The inbound DST processing section 82 then processesthe partial results 102 to produce a final result, or results 104. Forexample, if the task was to find a specific word or phrase within data,the partial results 102 indicate where in each of the prescribedportions of the data the corresponding DST execution units found thespecific word or phrase. The distributed task control module 188combines the individual partial results 102 for the correspondingportions of the data into a final result 104 for the data as a whole.

In another example of operation, the inbound DST processing section 82is retrieving stored data from the DST execution units (i.e., the DSTNmodule). In this example, the DST execution units output encoded dataslices 100 corresponding to the data retrieval requests. The de-groupingmodule 180 receives retrieved slices 100 and de-groups them to produceencoded data slices per data partition 122. The DS error decoding module182 decodes, in accordance with DS error encoding parameters, theencoded data slices per data partition 122 to produce data partitions120.

The data de-partitioning module 184 combines the data partitions 120into the data 92. The control module 186 controls the conversion ofretrieved slices 100 into the data 92 using control signals 190 to eachof the modules. For instance, the control module 186 providesde-grouping information to the de-grouping module 180, provides the DSerror encoding parameters to the DS error decoding module 182, andprovides de-partitioning information to the data de-partitioning module184.

FIG. 14 is a logic diagram of an example of a method that is executableby distributed storage and task (DST) client module regarding inboundDST processing. The method begins at step 194 where the DST clientmodule receives partial results. The method continues at step 196 wherethe DST client module retrieves the task corresponding to the partialresults. For example, the partial results include header informationthat identifies the requesting entity, which correlates to the requestedtask.

The method continues at step 198 where the DST client module determinesresult processing information based on the task. For example, if thetask were to identify a particular word or phrase within the data, theresult processing information would indicate to aggregate the partialresults for the corresponding portions of the data to produce the finalresult. As another example, if the task were to count the occurrences ofa particular word or phrase within the data, results of processing theinformation would indicate to add the partial results to produce thefinal results. The method continues at step 200 where the DST clientmodule processes the partial results in accordance with the resultprocessing information to produce the final result or results.

FIG. 15 is a diagram of an example of de-grouping selection processingof an inbound distributed storage and task (DST) processing section of aDST client module. In general, this is an inverse process of thegrouping module of the outbound DST processing section of FIG. 9 .Accordingly, for each data partition (e.g., partition #1), thede-grouping module retrieves the corresponding slice grouping from theDST execution units (EU) (e.g., DST 1-5).

As shown, DST execution unit #1 provides a first slice grouping, whichincludes the first encoded slices of each of the sets of encoded slices(e.g., encoded data slices of contiguous data of data blocks 1-15); DSTexecution unit #2 provides a second slice grouping, which includes thesecond encoded slices of each of the sets of encoded slices (e.g.,encoded data slices of contiguous data of data blocks 16-30); DSTexecution unit #3 provides a third slice grouping, which includes thethird encoded slices of each of the sets of encoded slices (e.g.,encoded data slices of contiguous data of data blocks 31-45); DSTexecution unit #4 provides a fourth slice grouping, which includes thefourth encoded slices of each of the sets of encoded slices (e.g., firstencoded data slices of error coding (EC) data); and DST execution unit#5 provides a fifth slice grouping, which includes the fifth encodedslices of each of the sets of encoded slices (e.g., first encoded dataslices of error coding (EC) data).

The de-grouping module de-groups the slice groupings (e.g., receivedslices 100) using a de-grouping selector 180 controlled by a controlsignal 190 as shown in the example to produce a plurality of sets ofencoded data slices (e.g., retrieved slices for a partition into sets ofslices 122). Each set corresponding to a data segment of the datapartition.

FIG. 16 is a schematic block diagram of an embodiment of a dispersedstorage (DS) error decoding module 182 of an inbound distributed storageand task (DST) processing section. The DS error decoding module 182includes an inverse per slice security processing module 202, ade-slicing module 204, an error decoding module 206, an inverse segmentsecurity module 208, a de-segmenting processing module 210, and acontrol module 186.

In an example of operation, the inverse per slice security processingmodule 202, when enabled by the control module 186, unsecures eachencoded data slice 122 based on slice de-security information receivedas control information 190 (e.g., the compliment of the slice securityinformation discussed with reference to FIG. 6 ) received from thecontrol module 186. The slice security information includes datadecompression, decryption, de-watermarking, integrity check (e.g., CRCverification, etc.), and/or any other type of digital security. Forexample, when the inverse per slice security processing module 202 isenabled, it verifies integrity information (e.g., a CRC value) of eachencoded data slice 122, it decrypts each verified encoded data slice,and decompresses each decrypted encoded data slice to produce sliceencoded data 158. When the inverse per slice security processing module202 is not enabled, it passes the encoded data slices 122 as the slicedencoded data 158 or is bypassed such that the retrieved encoded dataslices 122 are provided as the sliced encoded data 158.

The de-slicing module 204 de-slices the sliced encoded data 158 intoencoded data segments 156 in accordance with a pillar width of the errorcorrection encoding parameters received as control information 190 fromthe control module 186. For example, if the pillar width is five, thede-slicing module 204 de-slices a set of five encoded data slices intoan encoded data segment 156. The error decoding module 206 decodes theencoded data segments 156 in accordance with error correction decodingparameters received as control information 190 from the control module186 to produce secure data segments 154. The error correction decodingparameters include identifying an error correction encoding scheme(e.g., forward error correction algorithm, a Reed-Solomon basedalgorithm, an information dispersal algorithm, etc.), a pillar width, adecode threshold, a read threshold, a write threshold, etc. For example,the error correction decoding parameters identify a specific errorcorrection encoding scheme, specify a pillar width of five, and specifya decode threshold of three.

The inverse segment security processing module 208, when enabled by thecontrol module 186, unsecures the secured data segments 154 based onsegment security information received as control information 190 fromthe control module 186. The segment security information includes datadecompression, decryption, de-watermarking, integrity check (e.g., CRC,etc.) verification, and/or any other type of digital security. Forexample, when the inverse segment security processing module 208 isenabled, it verifies integrity information (e.g., a CRC value) of eachsecure data segment 154, it decrypts each verified secured data segment,and decompresses each decrypted secure data segment to produce a datasegment 152. When the inverse segment security processing module 208 isnot enabled, it passes the decoded data segment 154 as the data segment152 or is bypassed.

The de-segment processing module 210 receives the data segments 152 andreceives de-segmenting information as control information 190 from thecontrol module 186. The de-segmenting information indicates how thede-segment processing module 210 is to de-segment the data segments 152into a data partition 120. For example, the de-segmenting informationindicates how the rows and columns of data segments are to be rearrangedto yield the data partition 120.

FIG. 17 is a diagram of an example of de-slicing and error decodingprocessing of a dispersed error decoding module. A de-slicing module 204receives at least a decode threshold number of encoded data slices 158for each data segment in accordance with control information 190 andprovides encoded data 156. In this example, a decode threshold is three.As such, each set of encoded data slices 158 is shown to have threeencoded data slices per data segment. The de-slicing module 204 mayreceive three encoded data slices per data segment because an associateddistributed storage and task (DST) client module requested retrievingonly three encoded data slices per segment or selected three of theretrieved encoded data slices per data segment. As shown, which is basedon the unity matrix encoding previously discussed with reference to FIG.8 , an encoded data slice may be a data-based encoded data slice (e.g.,DS1_d 1&d 2) or an error code based encoded data slice (e.g., ES3_1).

An error decoding module 206 decodes the encoded data 156 of each datasegment in accordance with the error correction decoding parameters ofcontrol information 190 to produce secured segments 154. In thisexample, data segment 1 includes 3 rows with each row being treated asone word for encoding. As such, data segment 1 includes three words:word 1 including data blocks d 1 and d 2, word 2 including data blocks d16 and d 17, and word 3 including data blocks d 31 and d 32. Each ofdata segments 2 - 7 includes three words where each word includes twodata blocks. Data segment 8 includes three words where each wordincludes a single data block (e.g., d 15, d 30, and d 45).

FIG. 18 is a diagram of an example of de-segment processing of aninbound distributed storage and task (DST) processing. In this example,a de-segment processing module 210 receives data segments 152 (e.g.,1-8) and rearranges the data blocks of the data segments into rows andcolumns in accordance with de-segmenting information of controlinformation 190 to produce a data partition 120. Note that the number ofrows is based on the decode threshold (e.g., 3 in this specific example)and the number of columns is based on the number and size of the datablocks.

The de-segmenting module 210 converts the rows and columns of datablocks into the data partition 120. Note that each data block may be ofthe same size as other data blocks or of a different size. In addition,the size of each data block may be a few bytes to megabytes of data.

FIG. 19 is a diagram of an example of converting slice groups into data92 within an inbound distributed storage and task (DST) processingsection. As shown, the data 92 is reconstructed from a plurality of datapartitions (1-x, where x is an integer greater than 4). Each datapartition (or chunk set of data) is decoded and re-grouped using ade-grouping and decoding function 212 and a de-partition function 214from slice groupings as previously discussed. For a given datapartition, the slice groupings (e.g., at least a decode threshold perdata segment of encoded data slices) are received from DST executionunits. From data partition to data partition, the ordering of the slicegroupings received from the DST execution units may vary as discussedwith reference to FIG. 10 .

FIG. 20 is a diagram of an example of a distributed storage and/orretrieval within the distributed computing system. The distributedcomputing system includes a plurality of distributed storage and/or task(DST) processing client modules 34 (one shown) coupled to a distributedstorage and/or task processing network (DSTN) module, or multiple DSTNmodules, via a network 24. The DST client module 34 includes an outboundDST processing section 80 and an inbound DST processing section 82. TheDSTN module includes a plurality of DST execution units. Each DSTexecution unit includes a controller 86, memory 88, one or moredistributed task (DT) execution modules 90, and a DST client module 34.

In an example of data storage, the DST client module 34 has data 92 thatit desires to store in the DSTN module. The data 92 may be a file (e.g.,video, audio, text, graphics, etc.), a data object, a data block, anupdate to a file, an update to a data block, etc. In this instance, theoutbound DST processing module 80 converts the data 92 into encoded dataslices 216 as will be further described with reference to FIGS. 21-23 .The outbound DST processing module 80 sends, via the network 24, to theDST execution units for storage as further described with reference toFIG. 24 .

In an example of data retrieval, the DST client module 34 issues aretrieve request to the DST execution units for the desired data 92. Theretrieve request may address each DST executions units storing encodeddata slices of the desired data, address a decode threshold number ofDST execution units, address a read threshold number of DST executionunits, or address some other number of DST execution units. In responseto the request, each addressed DST execution unit retrieves its encodeddata slices 100 of the desired data and sends them to the inbound DSTprocessing section 82, via the network 24.

When, for each data segment, the inbound DST processing section 82receives at least a decode threshold number of encoded data slices 100,it converts the encoded data slices 100 into a data segment. The inboundDST processing section 82 aggregates the data segments to produce theretrieved data 92.

FIG. 21 is a schematic block diagram of an embodiment of an outbounddistributed storage and/or task (DST) processing section 80 of a DSTclient module coupled to a distributed storage and task network (DSTN)module (e.g., a plurality of DST execution units) via a network 24. Theoutbound DST processing section 80 includes a data partitioning module110, a dispersed storage (DS) error encoding module 112, a groupingselector module 114, a control module 116, and a distributed taskcontrol module 118.

In an example of operation, the data partitioning module 110 isby-passed such that data 92 is provided directly to the DS errorencoding module 112. The control module 116 coordinates the by-passingof the data partitioning module 110 by outputting a bypass 220 messageto the data partitioning module 110.

The DS error encoding module 112 receives the data 92 in a serialmanner, a parallel manner, and/or a combination thereof. The DS errorencoding module 112 DS error encodes the data in accordance with controlinformation 160 from the control module 116 to produce encoded dataslices 218. The DS error encoding includes segmenting the data 92 intodata segments, segment security processing (e.g., encryption,compression, watermarking, integrity check (e.g., CRC, etc.)), errorencoding, slicing, and/or per slice security processing (e.g.,encryption, compression, watermarking, integrity check (e.g., CRC,etc.)). The control information 160 indicates which steps of the DSerror encoding are active for the data 92 and, for active steps,indicates the parameters for the step. For example, the controlinformation 160 indicates that the error encoding is active and includeserror encoding parameters (e.g., pillar width, decode threshold, writethreshold, read threshold, type of error encoding, etc.).

The grouping selector module 114 groups the encoded slices 218 of thedata segments into pillars of slices 216. The number of pillarscorresponds to the pillar width of the DS error encoding parameters. Inthis example, the distributed task control module 118 facilitates thestorage request.

FIG. 22 is a schematic block diagram of an example of a dispersedstorage (DS) error encoding module 112 for the example of FIG. 21 . TheDS error encoding module 112 includes a segment processing module 142, asegment security processing module 144, an error encoding module 146, aslicing module 148, and a per slice security processing module 150. Eachof these modules is coupled to a control module 116 to receive controlinformation 160 therefrom.

In an example of operation, the segment processing module 142 receivesdata 92 and receives segmenting information as control information 160from the control module 116. The segmenting information indicates howthe segment processing module is to segment the data. For example, thesegmenting information indicates the size of each data segment. Thesegment processing module 142 segments the data 92 into data segments152 in accordance with the segmenting information.

The segment security processing module 144, when enabled by the controlmodule 116, secures the data segments 152 based on segment securityinformation received as control information 160 from the control module116. The segment security information includes data compression,encryption, watermarking, integrity check (e.g., CRC, etc.), and/or anyother type of digital security. For example, when the segment securityprocessing module 144 is enabled, it compresses a data segment 152,encrypts the compressed data segment, and generates a CRC value for theencrypted data segment to produce a secure data segment. When thesegment security processing module 144 is not enabled, it passes thedata segments 152 to the error encoding module 146 or is bypassed suchthat the data segments 152 are provided to the error encoding module146.

The error encoding module 146 encodes the secure data segments inaccordance with error correction encoding parameters received as controlinformation 160 from the control module 116. The error correctionencoding parameters include identifying an error correction encodingscheme (e.g., forward error correction algorithm, a Reed-Solomon basedalgorithm, an information dispersal algorithm, etc.), a pillar width, adecode threshold, a read threshold, a write threshold, etc. For example,the error correction encoding parameters identify a specific errorcorrection encoding scheme, specifies a pillar width of five, andspecifies a decode threshold of three. From these parameters, the errorencoding module 146 encodes a data segment to produce an encoded datasegment.

The slicing module 148 slices the encoded data segment in accordancewith a pillar width of the error correction encoding parameters. Forexample, if the pillar width is five, the slicing module slices anencoded data segment into a set of five encoded data slices. As such,for a plurality of data segments, the slicing module 148 outputs aplurality of sets of encoded data slices as shown within encoding andslicing function 222 as described.

The per slice security processing module 150, when enabled by thecontrol module 116, secures each encoded data slice based on slicesecurity information received as control information 160 from thecontrol module 116. The slice security information includes datacompression, encryption, watermarking, integrity check (e.g., CRC,etc.), and/or any other type of digital security. For example, when theper slice security processing module 150 is enabled, it may compress anencoded data slice, encrypt the compressed encoded data slice, andgenerate a CRC value for the encrypted encoded data slice to produce asecure encoded data slice tweaking. When the per slice securityprocessing module 150 is not enabled, it passes the encoded data slicesor is bypassed such that the encoded data slices 218 are the output ofthe DS error encoding module 112.

FIG. 23 is a diagram of an example of converting data 92 into pillarslice groups utilizing encoding, slicing and pillar grouping function224 for storage in memory of a distributed storage and task network(DSTN) module. As previously discussed the data 92 is encoded and slicedinto a plurality of sets of encoded data slices; one set per datasegment. The grouping selection module organizes the sets of encodeddata slices into pillars of data slices. In this example, the DS errorencoding parameters include a pillar width of 5 and a decode thresholdof 3. As such, for each data segment, 5 encoded data slices are created.

The grouping selection module takes the first encoded data slice of eachof the sets and forms a first pillar, which may be sent to the first DSTexecution unit. Similarly, the grouping selection module creates thesecond pillar from the second slices of the sets; the third pillar fromthe third slices of the sets; the fourth pillar from the fourth slicesof the sets; and the fifth pillar from the fifth slices of the set.

FIG. 24 is a schematic block diagram of an embodiment of a distributedstorage and/or task (DST) execution unit that includes an interface 169,a controller 86, memory 88, one or more distributed task (DT) executionmodules 90, and a DST client module 34. A computing core 26 may beutilized to implement the one or more DT execution modules 90 and theDST client module 34. The memory 88 is of sufficient size to store asignificant number of encoded data slices (e.g., thousands of slices tohundreds-of-millions of slices) and may include one or more hard drivesand/or one or more solid-state memory devices (e.g., flash memory, DRAM,etc.).

In an example of storing a pillar of slices 216, the DST execution unitreceives, via interface 169, a pillar of slices 216 (e.g., pillar #1slices). The memory 88 stores the encoded data slices 216 of the pillarof slices in accordance with memory control information 174 it receivesfrom the controller 86. The controller 86 (e.g., a processing module, aCPU, etc.) generates the memory control information 174 based ondistributed storage information (e.g., user information (e.g., user ID,distributed storage permissions, data access permission, etc.), vaultinformation (e.g., virtual memory assigned to user, user group, etc.),etc.). Similarly, when retrieving slices, the DST execution unitreceives, via interface 169, a slice retrieval request. The memory 88retrieves the slice in accordance with memory control information 174 itreceives from the controller 86. The memory 88 outputs the slice 100,via the interface 169, to a requesting entity.

FIG. 25 is a schematic block diagram of an example of operation of aninbound distributed storage and/or task (DST) processing section 82 forretrieving dispersed error encoded data 92. The inbound DST processingsection 82 includes a de-grouping module 180, a dispersed storage (DS)error decoding module 182, a data de-partitioning module 184, a controlmodule 186, and a distributed task control module 188. Note that thecontrol module 186 and/or the distributed task control module 188 may beseparate modules from corresponding ones of an outbound DST processingsection or may be the same modules.

In an example of operation, the inbound DST processing section 82 isretrieving stored data 92 from the DST execution units (i.e., the DSTNmodule). In this example, the DST execution units output encoded dataslices corresponding to data retrieval requests from the distributedtask control module 188. The de-grouping module 180 receives pillars ofslices 100 and de-groups them in accordance with control information 190from the control module 186 to produce sets of encoded data slices 218.The DS error decoding module 182 decodes, in accordance with the DSerror encoding parameters received as control information 190 from thecontrol module 186, each set of encoded data slices 218 to produce datasegments, which are aggregated into retrieved data 92. The datade-partitioning module 184 is by-passed in this operational mode via abypass signal 226 of control information 190 from the control module186.

FIG. 26 is a schematic block diagram of an embodiment of a dispersedstorage (DS) error decoding module 182 of an inbound distributed storageand task (DST) processing section. The DS error decoding module 182includes an inverse per slice security processing module 202, ade-slicing module 204, an error decoding module 206, an inverse segmentsecurity module 208, and a de-segmenting processing module 210. Thedispersed error decoding module 182 is operable to de-slice and decodeencoded slices per data segment 218 utilizing a de-slicing and decodingfunction 228 to produce a plurality of data segments that arede-segmented utilizing a de-segment function 230 to recover data 92.

In an example of operation, the inverse per slice security processingmodule 202, when enabled by the control module 186 via controlinformation 190, unsecures each encoded data slice 218 based on slicede-security information (e.g., the compliment of the slice securityinformation discussed with reference to FIG. 6 ) received as controlinformation 190 from the control module 186. The slice de-securityinformation includes data decompression, decryption, de-watermarking,integrity check (e.g., CRC verification, etc.), and/or any other type ofdigital security. For example, when the inverse per slice securityprocessing module 202 is enabled, it verifies integrity information(e.g., a CRC value) of each encoded data slice 218, it decrypts eachverified encoded data slice, and decompresses each decrypted encodeddata slice to produce slice encoded data. When the inverse per slicesecurity processing module 202 is not enabled, it passes the encodeddata slices 218 as the sliced encoded data or is bypassed such that theretrieved encoded data slices 218 are provided as the sliced encodeddata.

The de-slicing module 204 de-slices the sliced encoded data into encodeddata segments in accordance with a pillar width of the error correctionencoding parameters received as control information 190 from a controlmodule 186. For example, if the pillar width is five, the de-slicingmodule de-slices a set of five encoded data slices into an encoded datasegment. Alternatively, the encoded data segment may include just threeencoded data slices (e.g., when the decode threshold is 3).

The error decoding module 206 decodes the encoded data segments inaccordance with error correction decoding parameters received as controlinformation 190 from the control module 186 to produce secure datasegments. The error correction decoding parameters include identifyingan error correction encoding scheme (e.g., forward error correctionalgorithm, a Reed-Solomon based algorithm, an information dispersalalgorithm, etc.), a pillar width, a decode threshold, a read threshold,a write threshold, etc. For example, the error correction decodingparameters identify a specific error correction encoding scheme, specifya pillar width of five, and specify a decode threshold of three.

The inverse segment security processing module 208, when enabled by thecontrol module 186, unsecures the secured data segments based on segmentsecurity information received as control information 190 from thecontrol module 186. The segment security information includes datadecompression, decryption, de-watermarking, integrity check (e.g., CRC,etc.) verification, and/or any other type of digital security. Forexample, when the inverse segment security processing module is enabled,it verifies integrity information (e.g., a CRC value) of each securedata segment, it decrypts each verified secured data segment, anddecompresses each decrypted secure data segment to produce a datasegment 152. When the inverse segment security processing module 208 isnot enabled, it passes the decoded data segment 152 as the data segmentor is bypassed. The de-segmenting processing module 210 aggregates thedata segments 152 into the data 92 in accordance with controlinformation 190 from the control module 186.

FIG. 27 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module that includes aplurality of distributed storage and task (DST) execution units (#1through #n, where, for example, n is an integer greater than or equal tothree). Each of the DST execution units includes a DST client module 34,a controller 86, one or more DT (distributed task) execution modules 90,and memory 88.

In this example, the DSTN module stores, in the memory of the DSTexecution units, a plurality of DS (dispersed storage) encoded data(e.g., 1 through n, where n is an integer greater than or equal to two)and stores a plurality of DS encoded task codes (e.g., 1 through k,where k is an integer greater than or equal to two). The DS encoded datamay be encoded in accordance with one or more examples described withreference to FIGS. 3 - 19 (e.g., organized in slice groupings) orencoded in accordance with one or more examples described with referenceto FIGS. 20 - 26 (e.g., organized in pillar groups). The data that isencoded into the DS encoded data may be of any size and/or of anycontent. For example, the data may be one or more digital books, a copyof a company’s emails, a large-scale Internet search, a video securityfile, one or more entertainment video files (e.g., television programs,movies, etc.), data files, and/or any other large amount of data (e.g.,greater than a few Terabytes).

The tasks that are encoded into the DS encoded task code may be a simplefunction (e.g., a mathematical function, a logic function, an identifyfunction, a find function, a search engine function, a replace function,etc.), a complex function (e.g., compression, human and/or computerlanguage translation, text-to-voice conversion, voice-to-textconversion, etc.), multiple simple and/or complex functions, one or morealgorithms, one or more applications, etc. The tasks may be encoded intothe DS encoded task code in accordance with one or more examplesdescribed with reference to FIGS. 3 - 19 (e.g., organized in slicegroupings) or encoded in accordance with one or more examples describedwith reference to FIGS. 20 - 26 (e.g., organized in pillar groups).

In an example of operation, a DST client module of a user device or of aDST processing unit issues a DST request to the DSTN module. The DSTrequest may include a request to retrieve stored data, or a portionthereof, may include a request to store data that is included with theDST request, may include a request to perform one or more tasks onstored data, may include a request to perform one or more tasks on dataincluded with the DST request, etc. In the cases where the DST requestincludes a request to store data or to retrieve data, the client moduleand/or the DSTN module processes the request as previously discussedwith reference to one or more of FIGS. 3 - 19 (e.g., slice groupings)and/or 20 - 26 (e.g., pillar groupings). In the case where the DSTrequest includes a request to perform one or more tasks on data includedwith the DST request, the DST client module and/or the DSTN moduleprocess the DST request as previously discussed with reference to one ormore of FIGS. 3 - 19 .

In the case where the DST request includes a request to perform one ormore tasks on stored data, the DST client module and/or the DSTN moduleprocesses the DST request as will be described with reference to one ormore of FIGS. 28 - 39 . In general, the DST client module identifiesdata and one or more tasks for the DSTN module to execute upon theidentified data. The DST request may be for a one-time execution of thetask or for an on-going execution of the task. As an example of thelatter, as a company generates daily emails, the DST request may be todaily search new emails for inappropriate content and, if found, recordthe content, the email sender(s), the email recipient(s), email routinginformation, notify human resources of the identified email, etc.

FIG. 28 is a schematic block diagram of an example of a distributedcomputing system performing tasks on stored data. In this example, twodistributed storage and task (DST) client modules 1-2 are shown: thefirst may be associated with a user device and the second may beassociated with a DST processing unit or a high priority user device(e.g., high priority clearance user, system administrator, etc.). EachDST client module includes a list of stored data 234 and a list of taskscodes 236. The list of stored data 234 includes one or more entries ofdata identifying information, where each entry identifies data stored inthe DSTN module 22. The data identifying information (e.g., data ID)includes one or more of a data file name, a data file directory listing,DSTN addressing information of the data, a data object identifier, etc.The list of tasks 236 includes one or more entries of task codeidentifying information, when each entry identifies task codes stored inthe DSTN module 22. The task code identifying information (e.g., taskID) includes one or more of a task file name, a task file directorylisting, DSTN addressing information of the task, another type ofidentifier to identify the task, etc.

As shown, the list of data 234 and the list of tasks 236 are eachsmaller in number of entries for the first DST client module than thecorresponding lists of the second DST client module. This may occurbecause the user device associated with the first DST client module hasfewer privileges in the distributed computing system than the deviceassociated with the second DST client module. Alternatively, this mayoccur because the user device associated with the first DST clientmodule serves fewer users than the device associated with the second DSTclient module and is restricted by the distributed computing systemaccordingly. As yet another alternative, this may occur through norestraints by the distributed computing system, it just occurred becausethe operator of the user device associated with the first DST clientmodule has selected fewer data and/or fewer tasks than the operator ofthe device associated with the second DST client module.

In an example of operation, the first DST client module selects one ormore data entries 238 and one or more tasks 240 from its respectivelists (e.g., selected data ID and selected task ID). The first DSTclient module sends its selections to a task distribution module 232.The task distribution module 232 may be within a stand-alone device ofthe distributed computing system, may be within the user device thatcontains the first DST client module, or may be within the DSTN module22.

Regardless of the task distribution module’s location, it generates DSTallocation information 242 from the selected task ID 240 and theselected data ID 238. The DST allocation information 242 includes datapartitioning information, task execution information, and/orintermediate result information. The task distribution module 232 sendsthe DST allocation information 242 to the DSTN module 22. Note that oneor more examples of the DST allocation information will be discussedwith reference to one or more of FIGS. 29 - 39 .

The DSTN module 22 interprets the DST allocation information 242 toidentify the stored DS encoded data (e.g., DS error encoded data 2) andto identify the stored DS error encoded task code (e.g., DS errorencoded task code 1). In addition, the DSTN module 22 interprets the DSTallocation information 242 to determine how the data is to bepartitioned and how the task is to be partitioned. The DSTN module 22also determines whether the selected DS error encoded data 238 needs tobe converted from pillar grouping to slice grouping. If so, the DSTNmodule 22 converts the selected DS error encoded data into slicegroupings and stores the slice grouping DS error encoded data byoverwriting the pillar grouping DS error encoded data or by storing itin a different location in the memory of the DSTN module 22 (i.e., doesnot overwrite the pillar grouping DS encoded data).

The DSTN module 22 partitions the data and the task as indicated in theDST allocation information 242 and sends the portions to selected DSTexecution units of the DSTN module 22. Each of the selected DSTexecution units performs its partial task(s) on its slice groupings toproduce partial results. The DSTN module 22 collects the partial resultsfrom the selected DST execution units and provides them, as resultinformation 244, to the task distribution module. The result information244 may be the collected partial results, one or more final results asproduced by the DSTN module 22 from processing the partial results inaccordance with the DST allocation information 242, or one or moreintermediate results as produced by the DSTN module 22 from processingthe partial results in accordance with the DST allocation information242.

The task distribution module 232 receives the result information 244 andprovides one or more final results 104 therefrom to the first DST clientmodule. The final result(s) 104 may be result information 244 or aresult(s) of the task distribution module’s processing of the resultinformation 244.

In concurrence with processing the selected task of the first DST clientmodule, the distributed computing system may process the selectedtask(s) of the second DST client module on the selected data(s) of thesecond DST client module. Alternatively, the distributed computingsystem may process the second DST client module’s request subsequent to,or preceding, that of the first DST client module. Regardless of theordering and/or parallel processing of the DST client module requests,the second DST client module provides its selected data 238 and selectedtask 240 to a task distribution module 232. If the task distributionmodule 232 is a separate device of the distributed computing system orwithin the DSTN module, the task distribution modules 232 coupled to thefirst and second DST client modules may be the same module. The taskdistribution module 232 processes the request of the second DST clientmodule in a similar manner as it processed the request of the first DSTclient module.

FIG. 29 is a schematic block diagram of an embodiment of a taskdistribution module 232 facilitating the example of FIG. 28 . The taskdistribution module 232 includes a plurality of tables it uses togenerate distributed storage and task (DST) allocation information 242for selected data and selected tasks received from a DST client module.The tables include data storage information 248, task storageinformation 250, distributed task (DT) execution module information 252,and task ⇔ sub-task mapping information 246.

The data storage information table 248 includes a data identification(ID) field 260, a data size field 262, an addressing information field264, distributed storage (DS) information 266, and may further includeother information regarding the data, how it is stored, and/or how itcan be processed. For example, DS encoded data #1 has a data ID of 1, adata size of AA (e.g., a byte size of a few Terabytes or more),addressing information of Addr_1_AA, and DS parameters of ⅗; SEG_1; andSLC_1. In this example, the addressing information may be a virtualaddress corresponding to the virtual address of the first storage word(e.g., one or more bytes) of the data and information on how tocalculate the other addresses, may be a range of virtual addresses forthe storage words of the data, physical addresses of the first storageword or the storage words of the data, may be a list of slice names ofthe encoded data slices of the data, etc. The DS parameters may includeidentity of an error encoding scheme, decode threshold/pillar width(e.g., ⅗ for the first data entry), segment security information (e.g.,SEG_1), per slice security information (e.g., SLC_1), and/or any otherinformation regarding how the data was encoded into data slices.

The task storage information table 250 includes a task identification(ID) field 268, a task size field 270, an addressing information field272, distributed storage (DS) information 274, and may further includeother information regarding the task, how it is stored, and/or how itcan be used to process data. For example, DS encoded task #2 has a taskID of 2, a task size of XY, addressing information of Addr_2_XY, and DSparameters of ⅗; SEG_2; and SLC_2. In this example, the addressinginformation may be a virtual address corresponding to the virtualaddress of the first storage word (e.g., one or more bytes) of the taskand information on how to calculate the other addresses, may be a rangeof virtual addresses for the storage words of the task, physicaladdresses of the first storage word or the storage words of the task,may be a list of slices names of the encoded slices of the task code,etc. The DS parameters may include identity of an error encoding scheme,decode threshold/pillar width (e.g., ⅗ for the first data entry),segment security information (e.g., SEG_2), per slice securityinformation (e.g., SLC_2), and/or any other information regarding howthe task was encoded into encoded task slices. Note that the segmentand/or the per-slice security information include a type of encryption(if enabled), a type of compression (if enabled), watermarkinginformation (if enabled), and/or an integrity check scheme (if enabled).

The task ⇔ sub-task mapping information table 246 includes a task field256 and a sub-task field 258. The task field 256 identifies a taskstored in the memory of a distributed storage and task network (DSTN)module and the corresponding sub-task fields 258 indicates whether thetask includes sub-tasks and, if so, how many and if any of the sub-tasksare ordered. In this example, the task ⇔ sub-task mapping informationtable 246 includes an entry for each task stored in memory of the DSTNmodule (e.g., task 1 through task k). In particular, this exampleindicates that task 1 includes 7 sub-tasks; task 2 does not includesub-tasks, and task k includes r number of sub-tasks (where r is aninteger greater than or equal to two).

The DT execution module table 252 includes a DST execution unit ID field276, a DT execution module ID field 278, and a DT execution modulecapabilities field 280. The DST execution unit ID field 276 includes theidentity of DST units in the DSTN module. The DT execution module IDfield 278 includes the identity of each DT execution unit in each DSTunit. For example, DST unit 1 includes three DT executions modules(e.g., 1_1, 1_2, and 1_3). The DT execution capabilities field 280includes identity of the capabilities of the corresponding DT executionunit. For example, DT execution module 1_1 includes capabilities X,where X includes one or more of MIPS capabilities, processing resources(e.g., quantity and capability of microprocessors, CPUs, digital signalprocessors, co-processor, microcontrollers, arithmetic logic circuitry,and/or any other analog and/or digital processing circuitry),availability of the processing resources, memory information (e.g.,type, size, availability, etc.), and/or any information germane toexecuting one or more tasks.

From these tables, the task distribution module 232 generates the DSTallocation information 242 to indicate where the data is stored, how topartition the data, where the task is stored, how to partition the task,which DT execution units should perform which partial task on which datapartitions, where and how intermediate results are to be stored, etc. Ifmultiple tasks are being performed on the same data or different data,the task distribution module factors such information into itsgeneration of the DST allocation information.

FIG. 30 is a diagram of a specific example of a distributed computingsystem performing tasks on stored data as a task flow 318. In thisexample, selected data 92 is data 2 and selected tasks are tasks 1, 2,and 3. Task 1 corresponds to analyzing translation of data from onelanguage to another (e.g., human language or computer language); task 2corresponds to finding specific words and/or phrases in the data; andtask 3 corresponds to finding specific translated words and/or phrasesin translated data.

In this example, task 1 includes 7 sub-tasks: task 1_1 — identifynon-words (non-ordered); task 1_2 — identify unique words (non-ordered);task 1_3 — translate (non-ordered); task 1_4 — translate back (orderedafter task 1_3); task 1_5 — compare to ID errors (ordered after task1-4); task 1_6 — determine non-word translation errors (ordered aftertask 1_5 and 1_1); and task 1_7 — determine correct translations(ordered after 1_5 and 1_2). The sub-task further indicates whether theyare an ordered task (i.e., are dependent on the outcome of another task)or non-order (i.e., are independent of the outcome of another task).Task 2 does not include sub-tasks and task 3 includes two sub-tasks:task 3_1 translate; and task 3_2 find specific word or phrase intranslated data.

In general, the three tasks collectively are selected to analyze datafor translation accuracies, translation errors, translation anomalies,occurrence of specific words or phrases in the data, and occurrence ofspecific words or phrases on the translated data. Graphically, the data92 is translated 306 into translated data 282; is analyzed for specificwords and/or phrases 300 to produce a list of specific words and/orphrases 286; is analyzed for non-words 302 (e.g., not in a referencedictionary) to produce a list of non-words 290; and is analyzed forunique words 316 included in the data 92 (i.e., how many different wordsare included in the data) to produce a list of unique words 298. Each ofthese tasks is independent of each other and can therefore be processedin parallel if desired.

The translated data 282 is analyzed (e.g., sub-task 3_2) for specifictranslated words and/or phrases 304 to produce a list of specifictranslated words and/or phrases 288. The translated data 282 istranslated back 308 (e.g., sub-task 1_4) into the language of theoriginal data to produce re-translated data 284. These two tasks aredependent on the translate task (e.g., task 1_3) and thus must beordered after the translation task, which may be in a pipelined orderingor a serial ordering. The re-translated data 284 is then compared 310with the original data 92 to find words and/or phrases that did nottranslate (one way and/or the other) properly to produce a list ofincorrectly translated words 294. As such, the comparing task (e.g.,sub-task 1_5) 310 is ordered after the translation 306 andre-translation tasks 308 (e.g., sub-tasks 1_3 and 1_4).

The list of words incorrectly translated 294 is compared 312 to the listof non-words 290 to identify words that were not properly translatedbecause the words are non-words to produce a list of errors due tonon-words 292. In addition, the list of words incorrectly translated 294is compared 314 to the list of unique words 298 to identify unique wordsthat were properly translated to produce a list of correctly translatedwords 296. The comparison may also identify unique words that were notproperly translated to produce a list of unique words that were notproperly translated. Note that each list of words (e.g., specific wordsand/or phrases, non-words, unique words, translated words and/orphrases, etc.,) may include the word and/or phrase, how many times it isused, where in the data it is used, and/or any other informationrequested regarding a word and/or phrase.

FIG. 31 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module storing data and taskcodes for the example of FIG. 30 . As shown, DS encoded data 2 is storedas encoded data slices across the memory (e.g., stored in memories 88)of DST execution units 1 - 5; the DS encoded task code 1 (of task 1) andDS encoded task 3 are stored as encoded task slices across the memory ofDST execution units 1 - 5; and DS encoded task code 2 (of task 2) isstored as encoded task slices across the memory of DST execution units3-7. As indicated in the data storage information table and the taskstorage information table of FIG. 29 , the respective data/task has DSparameters of ⅗ for their decode threshold/pillar width; hence spanningthe memory of five DST execution units.

FIG. 32 is a diagram of an example of distributed storage and task (DST)allocation information 242 for the example of FIG. 30 . The DSTallocation information 242 includes data partitioning information 320,task execution information 322, and intermediate result information 324.The data partitioning information 320 includes the data identifier (ID),the number of partitions to split the data into, address information foreach data partition, and whether the DS encoded data has to betransformed from pillar grouping to slice grouping. The task executioninformation 322 includes tabular information having a taskidentification field 326, a task ordering field 328, a data partitionfield ID 330, and a set of DT execution modules 332 to use for thedistributed task processing per data partition. The intermediate resultinformation 324 includes tabular information having a name ID field 334,an ID of the DST execution unit assigned to process the correspondingintermediate result 336, a scratch pad storage field 338, and anintermediate result storage field 340.

Continuing with the example of FIG. 30 , where tasks 1-3 are to bedistributedly performed on data 2, the data partitioning informationincludes the ID of data 2. In addition, the task distribution moduledetermines whether the DS encoded data 2 is in the proper format fordistributed computing (e.g., was stored as slice groupings). If not, thetask distribution module indicates that the DS encoded data 2 formatneeds to be changed from the pillar grouping format to the slicegrouping format, which will be done by the DSTN module. In addition, thetask distribution module determines the number of partitions to dividethe data into (e.g., 2_1 through 2_z) and addressing information foreach partition.

The task distribution module generates an entry in the task executioninformation section for each sub-task to be performed. For example, task1_1 (e.g., identify non-words on the data) has no task ordering (i.e.,is independent of the results of other sub-tasks), is to be performed ondata partitions 2_1 through 2_z by DT execution modules 1_1, 2_1, 3_1,4_1, and 5_1. For instance, DT execution modules 1_1, 2_1, 3_1, 4_1, and5_1 search for non-words in data partitions 2_1 through 2_z to producetask 1_1 intermediate results (R1-1, which is a list of non-words). Task1_2(e.g., identify unique words) has similar task execution informationas task 1_1 to produce task 1_2 intermediate results (R1-2, which is thelist of unique words).

Task 1_3 (e.g., translate) includes task execution information as beingnon-ordered (i.e., is independent), having DT execution modules 1_1,2_1, 3_1, 4_1, and 5_1 translate data partitions 2_1 through 2_4 andhaving DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2 translate datapartitions 2_5 through 2_z to produce task 1_3 intermediate results(R1-3, which is the translated data). In this example, the datapartitions are grouped, where different sets of DT execution modulesperform a distributed sub-task (or task) on each data partition group,which allows for further parallel processing.

Task 1_4 (e.g., translate back) is ordered after task 1_3 and is to beexecuted on task 1_3′s intermediate result (e.g., R1-3_1) (e.g., thetranslated data). DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1 areallocated to translate back task 1_3 intermediate result partitionsR1-3_1 through R1-3_4 and DT execution modules 1_2, 2_2, 6_1, 7_1, and7_2 are allocated to translate back task 1_3 intermediate resultpartitions R1-3_5 through R1-3_z to produce task 1-4 intermediateresults (R1-4, which is the translated back data).

Task 1_5 (e.g., compare data and translated data to identify translationerrors) is ordered after task 1_4 and is to be executed on task 1_4′sintermediate results (R4-1) and on the data. DT execution modules 1_1,2_1, 3_1, 4_1, and 5_1 are allocated to compare the data partitions (2_1through 2_z) with partitions of task 1-4 intermediate results partitionsR1-4_1 through R1-4_z to produce task 1_5 intermediate results (R1-5,which is the list words translated incorrectly).

Task 1_6 (e.g., determine non-word translation errors) is ordered aftertasks 1_1 and 1_5 and is to be executed on tasks 1_1′s and 1_5′sintermediate results (R1-1 and R1-5). DT execution modules 1_1, 2_1,3_1, 4_1, and 5_1 are allocated to compare the partitions of task 1_1intermediate results (R1-1_1 through R1-1_z) with partitions of task 1-5intermediate results partitions (R1-5_1 through R1-5_z) to produce task1_6 intermediate results (R1-6, which is the list translation errors dueto non-words).

Task 1_7 (e.g., determine words correctly translated) is ordered aftertasks 1_2 and 1_5 and is to be executed on tasks 1_2′s and 1_5′sintermediate results (R1-1 and R1-5). DT execution modules 1_2, 2_2,3_2, 4_2, and 5_2 are allocated to compare the partitions of task 1_2intermediate results (R1-2_1 through R1-2_z) with partitions of task 1-5intermediate results partitions (R1-5_1 through R1-5_z) to produce task1_7 intermediate results (R1-7, which is the list of correctlytranslated words).

Task 2 (e.g., find specific words and/or phrases) has no task ordering(i.e., is independent of the results of other sub-tasks), is to beperformed on data partitions 2_1 through 2_z by DT execution modules3_1, 4_1, 5_1, 6_1, and 7_1. For instance, DT execution modules 3_1,4_1, 5_1, 6_1, and 7_1 search for specific words and/or phrases in datapartitions 2_1 through 2_z to produce task 2 intermediate results (R2,which is a list of specific words and/or phrases).

Task 3_2 (e.g., find specific translated words and/or phrases) isordered after task 1_3 (e.g., translate) is to be performed onpartitions R1-3_1 through R1-3_z by DT execution modules 1_2, 2_2, 3_2,4_2, and 5_2. For instance, DT execution modules 1_2, 2_2, 3_2, 4_2, and5_2 search for specific translated words and/or phrases in thepartitions of the translated data (R1-3_1 through R1-3_z) to producetask 3_2 intermediate results (R3-2, which is a list of specifictranslated words and/or phrases).

For each task, the intermediate result information indicates which DSTunit is responsible for overseeing execution of the task and, if needed,processing the partial results generated by the set of allocated DTexecution units. In addition, the intermediate result informationindicates a scratch pad memory for the task and where the correspondingintermediate results are to be stored. For example, for intermediateresult R1-1 (the intermediate result of task 1_1), DST unit 1 isresponsible for overseeing execution of the task 1_1 and coordinatesstorage of the intermediate result as encoded intermediate result slicesstored in memory of DST execution units 1-5. In general, the scratch padis for storing non-DS encoded intermediate results and the intermediateresult storage is for storing DS encoded intermediate results.

FIGS. 33 - 38 are schematic block diagrams of the distributed storageand task network (DSTN) module performing the example of FIG. 30 . InFIG. 33 , the DSTN module accesses the data 92 and partitions it into aplurality of partitions 1-z in accordance with distributed storage andtask network (DST) allocation information. For each data partition, theDSTN identifies a set of its DT (distributed task) execution modules 90to perform the task (e.g., identify non-words (i.e., not in a referencedictionary) within the data partition) in accordance with the DSTallocation information. From data partition to data partition, the setof DT execution modules 90 may be the same, different, or a combinationthereof (e.g., some data partitions use the same set while other datapartitions use different sets).

For the first data partition, the first set of DT execution modules(e.g., 1_1, 2_1, 3_1, 4_1, and 5_1 per the DST allocation information ofFIG. 32 ) executes task 1_1 to produce a first partial result 102 ofnon-words found in the first data partition. The second set of DTexecution modules (e.g., 1_1, 2_1, 3_1, 4_1, and 5_1 per the DSTallocation information of FIG. 32 ) executes task 1_1 to produce asecond partial result 102 of non-words found in the second datapartition. The sets of DT execution modules (as per the DST allocationinformation) perform task 1_1 on the data partitions until the “z” setof DT execution modules performs task 1_1 on the “zth” data partition toproduce a “zth” partial result 102 of non-words found in the “zth” datapartition.

As indicated in the DST allocation information of FIG. 32 , DSTexecution unit 1 is assigned to process the first through “zth” partialresults to produce the first intermediate result (R1-1), which is a listof non-words found in the data. For instance, each set of DT executionmodules 90 stores its respective partial result in the scratchpad memoryof DST execution unit 1 (which is identified in the DST allocation ormay be determined by DST execution unit 1). A processing module of DSTexecution 1 is engaged to aggregate the first through “zth” partialresults to produce the first intermediate result (e.g., R1_1). Theprocessing module stores the first intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 1.

DST execution unit 1 engages its DST client module to slice groupingbased DS error encode the first intermediate result (e.g., the list ofnon-words). To begin the encoding, the DST client module determineswhether the list of non-words is of a sufficient size to partition(e.g., greater than a Terabyte). If yes, it partitions the firstintermediate result (R1-1) into a plurality of partitions (e.g., R1-1_1through R1-1_m). If the first intermediate result is not of sufficientsize to partition, it is not partitioned.

For each partition of the first intermediate result, or for the firstintermediate result, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes ⅗decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-5).

In FIG. 34 , the DSTN module is performing task 1_2(e.g., find uniquewords) on the data 92. To begin, the DSTN module accesses the data 92and partitions it into a plurality of partitions 1-z in accordance withthe DST allocation information or it may use the data partitions of task1_1 if the partitioning is the same. For each data partition, the DSTNidentifies a set of its DT execution modules to perform task 1_2 inaccordance with the DST allocation information. From data partition todata partition, the set of DT execution modules may be the same,different, or a combination thereof. For the data partitions, theallocated set of DT execution modules executes task 1_2 to produce apartial results (e.g., 1^(st) through “zth”) of unique words found inthe data partitions.

As indicated in the DST allocation information of FIG. 32 , DSTexecution unit 1 is assigned to process the first through “zth” partialresults 102 of task 1_2 to produce the second intermediate result(R1-2), which is a list of unique words found in the data 92. Theprocessing module of DST execution 1 is engaged to aggregate the firstthrough “zth” partial results of unique words to produce the secondintermediate result. The processing module stores the secondintermediate result as non-DS error encoded data in the scratchpadmemory or in another section of memory of DST execution unit 1.

DST execution unit 1 engages its DST client module to slice groupingbased DS error encode the second intermediate result (e.g., the list ofnon-words). To begin the encoding, the DST client module determineswhether the list of unique words is of a sufficient size to partition(e.g., greater than a Terabyte). If yes, it partitions the secondintermediate result (R1-2) into a plurality of partitions (e.g., R1-2_1through R1-2_m). If the second intermediate result is not of sufficientsize to partition, it is not partitioned.

For each partition of the second intermediate result, or for the secondintermediate results, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes ⅗decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-5).

In FIG. 35 , the DSTN module is performing task 1_3 (e.g., translate) onthe data 92. To begin, the DSTN module accesses the data 92 andpartitions it into a plurality of partitions 1-z in accordance with theDST allocation information or it may use the data partitions of task 1_1if the partitioning is the same. For each data partition, the DSTNidentifies a set of its DT execution modules to perform task 1_3 inaccordance with the DST allocation information (e.g., DT executionmodules 1_1, 2_1, 3_1, 4_1, and 5_1 translate data partitions 2_1through 2_4 and DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2translate data partitions 2_5 through 2_z). For the data partitions, theallocated set of DT execution modules 90 executes task 1_3 to producepartial results 102 (e.g., 1^(st) through “zth”) of translated data.

As indicated in the DST allocation information of FIG. 32 , DSTexecution unit 2 is assigned to process the first through “zth” partialresults of task 1_3 to produce the third intermediate result (R1-3),which is translated data. The processing module of DST execution 2 isengaged to aggregate the first through “zth” partial results oftranslated data to produce the third intermediate result. The processingmodule stores the third intermediate result as non-DS error encoded datain the scratchpad memory or in another section of memory of DSTexecution unit 2.

DST execution unit 2 engages its DST client module to slice groupingbased DS error encode the third intermediate result (e.g., translateddata). To begin the encoding, the DST client module partitions the thirdintermediate result (R1-3) into a plurality of partitions (e.g., R1-3_1through R1-3_y). For each partition of the third intermediate result,the DST client module uses the DS error encoding parameters of the data(e.g., DS parameters of data 2, which includes ⅗ decode threshold/pillarwidth ratio) to produce slice groupings. The slice groupings are storedin the intermediate result memory (e.g., allocated memory in thememories of DST execution units 2-6 per the DST allocation information).

As is further shown in FIG. 35 , the DSTN module is performing task 1_4(e.g., retranslate) on the translated data of the third intermediateresult. To begin, the DSTN module accesses the translated data (from thescratchpad memory or from the intermediate result memory and decodes it)and partitions it into a plurality of partitions in accordance with theDST allocation information. For each partition of the third intermediateresult, the DSTN identifies a set of its DT execution modules 90 toperform task 1_4 in accordance with the DST allocation information(e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1 are allocated totranslate back partitions R1-3_1 through R1-3_4 and DT execution modules1_2, 2_2, 6_1, 7_1, and 7_2 are allocated to translate back partitionsR1-3_5 through R1-3_z). For the partitions, the allocated set of DTexecution modules executes task 1_4 to produce partial results 102(e.g., 1^(st) through “zth”) of re-translated data.

As indicated in the DST allocation information of FIG. 32 , DSTexecution unit 3 is assigned to process the first through “zth” partialresults of task 1_4 to produce the fourth intermediate result (R1-4),which is retranslated data. The processing module of DST execution 3 isengaged to aggregate the first through “zth” partial results ofretranslated data to produce the fourth intermediate result. Theprocessing module stores the fourth intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 3.

DST execution unit 3 engages its DST client module to slice groupingbased DS error encode the fourth intermediate result (e.g., retranslateddata). To begin the encoding, the DST client module partitions thefourth intermediate result (R1-4) into a plurality of partitions (e.g.,R1-4_1 through R1-4_z). For each partition of the fourth intermediateresult, the DST client module uses the DS error encoding parameters ofthe data (e.g., DS parameters of data 2, which includes ⅗ decodethreshold/pillar width ratio) to produce slice groupings. The slicegroupings are stored in the intermediate result memory (e.g., allocatedmemory in the memories of DST execution units 3-7 per the DST allocationinformation).

In FIG. 36 , a distributed storage and task network (DSTN) module isperforming task 1_5 (e.g., compare) on data 92 and retranslated data ofFIG. 35 . To begin, the DSTN module accesses the data 92 and partitionsit into a plurality of partitions in accordance with the DST allocationinformation or it may use the data partitions of task 1_1 if thepartitioning is the same. The DSTN module also accesses the retranslateddata from the scratchpad memory, or from the intermediate result memoryand decodes it, and partitions it into a plurality of partitions inaccordance with the DST allocation information. The number of partitionsof the retranslated data corresponds to the number of partitions of thedata.

For each pair of partitions (e.g., data partition 1 and retranslateddata partition 1), the DSTN identifies a set of its DT execution modules90 to perform task 1_5 in accordance with the DST allocation information(e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1). For each pairof partitions, the allocated set of DT execution modules executes task1_5 to produce partial results 102 (e.g., 1^(st) through “zth”) of alist of incorrectly translated words and/or phrases.

As indicated in the DST allocation information of FIG. 32 , DSTexecution unit 1 is assigned to process the first through “zth” partialresults of task 1_5 to produce the fifth intermediate result (R1-5),which is the list of incorrectly translated words and/or phrases. Inparticular, the processing module of DST execution 1 is engaged toaggregate the first through “zth” partial results of the list ofincorrectly translated words and/or phrases to produce the fifthintermediate result. The processing module stores the fifth intermediateresult as non-DS error encoded data in the scratchpad memory or inanother section of memory of DST execution unit 1.

DST execution unit 1 engages its DST client module to slice groupingbased DS error encode the fifth intermediate result. To begin theencoding, the DST client module partitions the fifth intermediate result(R1-5) into a plurality of partitions (e.g., R1-5_1 through R1-5_z). Foreach partition of the fifth intermediate result, the DST client moduleuses the DS error encoding parameters of the data (e.g., DS parametersof data 2, which includes ⅗ decode threshold/pillar width ratio) toproduce slice groupings. The slice groupings are stored in theintermediate result memory (e.g., allocated memory in the memories ofDST execution units 1-5 per the DST allocation information).

As is further shown in FIG. 36 , the DSTN module is performing task 1_6(e.g., translation errors due to non-words) on the list of incorrectlytranslated words and/or phrases (e.g., the fifth intermediate resultR1-5) and the list of non-words (e.g., the first intermediate resultR1-1). To begin, the DSTN module accesses the lists and partitions theminto a corresponding number of partitions.

For each pair of partitions (e.g., partition R1-1_1 and partitionR1-5_1), the DSTN identifies a set of its DT execution modules 90 toperform task 1_6 in accordance with the DST allocation information(e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1). For each pairof partitions, the allocated set of DT execution modules executes task1_6 to produce partial results 102 (e.g., 1^(st) through “zth”) of alist of incorrectly translated words and/or phrases due to non-words.

As indicated in the DST allocation information of FIG. 32 , DSTexecution unit 2 is assigned to process the first through “zth” partialresults of task 1_6 to produce the sixth intermediate result (R1-6),which is the list of incorrectly translated words and/or phrases due tonon-words. In particular, the processing module of DST execution 2 isengaged to aggregate the first through “zth” partial results of the listof incorrectly translated words and/or phrases due to non-words toproduce the sixth intermediate result. The processing module stores thesixth intermediate result as non-DS error encoded data in the scratchpadmemory or in another section of memory of DST execution unit 2.

DST execution unit 2 engages its DST client module to slice groupingbased DS error encode the sixth intermediate result. To begin theencoding, the DST client module partitions the sixth intermediate result(R1-6) into a plurality of partitions (e.g., R1-6_1 through R1-6_z). Foreach partition of the sixth intermediate result, the DST client moduleuses the DS error encoding parameters of the data (e.g., DS parametersof data 2, which includes ⅗ decode threshold/pillar width ratio) toproduce slice groupings. The slice groupings are stored in theintermediate result memory (e.g., allocated memory in the memories ofDST execution units 2-6 per the DST allocation information).

As is still further shown in FIG. 36 , the DSTN module is performingtask 1_7 (e.g., correctly translated words and/or phrases) on the listof incorrectly translated words and/or phrases (e.g., the fifthintermediate result R1-5) and the list of unique words (e.g., the secondintermediate result R1-2). To begin, the DSTN module accesses the listsand partitions them into a corresponding number of partitions.

For each pair of partitions (e.g., partition R1-2_1 and partitionR1-5_1), the DSTN identifies a set of its DT execution modules 90 toperform task 1_7 in accordance with the DST allocation information(e.g., DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2). For each pairof partitions, the allocated set of DT execution modules executes task1_7 to produce partial results 102 (e.g., 1^(st) through “zth”) of alist of correctly translated words and/or phrases.

As indicated in the DST allocation information of FIG. 32 , DSTexecution unit 3 is assigned to process the first through “zth” partialresults of task 1_7 to produce the seventh intermediate result (R1-7),which is the list of correctly translated words and/or phrases. Inparticular, the processing module of DST execution 3 is engaged toaggregate the first through “zth” partial results of the list ofcorrectly translated words and/or phrases to produce the seventhintermediate result. The processing module stores the seventhintermediate result as non-DS error encoded data in the scratchpadmemory or in another section of memory of DST execution unit 3.

DST execution unit 3 engages its DST client module to slice groupingbased DS error encode the seventh intermediate result. To begin theencoding, the DST client module partitions the seventh intermediateresult (R1-7) into a plurality of partitions (e.g., R1-7_1 throughR1-7_z). For each partition of the seventh intermediate result, the DSTclient module uses the DS error encoding parameters of the data (e.g.,DS parameters of data 2, which includes ⅗ decode threshold/pillar widthratio) to produce slice groupings. The slice groupings are stored in theintermediate result memory (e.g., allocated memory in the memories ofDST execution units 3-7 per the DST allocation information).

In FIG. 37 , the distributed storage and task network (DSTN) module isperforming task 2 (e.g., find specific words and/or phrases) on the data92. To begin, the DSTN module accesses the data and partitions it into aplurality of partitions 1-z in accordance with the DST allocationinformation or it may use the data partitions of task 1_1 if thepartitioning is the same. For each data partition, the DSTN identifies aset of its DT execution modules 90 to perform task 2 in accordance withthe DST allocation information. From data partition to data partition,the set of DT execution modules may be the same, different, or acombination thereof. For the data partitions, the allocated set of DTexecution modules executes task 2 to produce partial results 102 (e.g.,1^(st) through “zth”) of specific words and/or phrases found in the datapartitions.

As indicated in the DST allocation information of FIG. 32 , DSTexecution unit 7 is assigned to process the first through “zth” partialresults of task 2 to produce task 2 intermediate result (R2), which is alist of specific words and/or phrases found in the data. The processingmodule of DST execution 7 is engaged to aggregate the first through“zth” partial results of specific words and/or phrases to produce thetask 2 intermediate result. The processing module stores the task 2intermediate result as non-DS error encoded data in the scratchpadmemory or in another section of memory of DST execution unit 7.

DST execution unit 7 engages its DST client module to slice groupingbased DS error encode the task 2 intermediate result. To begin theencoding, the DST client module determines whether the list of specificwords and/or phrases is of a sufficient size to partition (e.g., greaterthan a Terabyte). If yes, it partitions the task 2 intermediate result(R2) into a plurality of partitions (e.g., R2_1 through R2_m). If thetask 2 intermediate result is not of sufficient size to partition, it isnot partitioned.

For each partition of the task 2 intermediate result, or for the task 2intermediate results, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes ⅗decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-4, and 7).

In FIG. 38 , the distributed storage and task network (DSTN) module isperforming task 3 (e.g., find specific translated words and/or phrases)on the translated data (R1-3). To begin, the DSTN module accesses thetranslated data (from the scratchpad memory or from the intermediateresult memory and decodes it) and partitions it into a plurality ofpartitions in accordance with the DST allocation information. For eachpartition, the DSTN identifies a set of its DT execution modules toperform task 3 in accordance with the DST allocation information. Frompartition to partition, the set of DT execution modules may be the same,different, or a combination thereof. For the partitions, the allocatedset of DT execution modules 90 executes task 3 to produce partialresults 102 (e.g., 1^(st) through “zth”) of specific translated wordsand/or phrases found in the data partitions.

As indicated in the DST allocation information of FIG. 32 , DSTexecution unit 5 is assigned to process the first through “zth” partialresults of task 3 to produce task 3 intermediate result (R3), which is alist of specific translated words and/or phrases found in the translateddata. In particular, the processing module of DST execution 5 is engagedto aggregate the first through “zth” partial results of specifictranslated words and/or phrases to produce the task 3 intermediateresult. The processing module stores the task 3 intermediate result asnon-DS error encoded data in the scratchpad memory or in another sectionof memory of DST execution unit 7.

DST execution unit 5 engages its DST client module to slice groupingbased DS error encode the task 3 intermediate result. To begin theencoding, the DST client module determines whether the list of specifictranslated words and/or phrases is of a sufficient size to partition(e.g., greater than a Terabyte). If yes, it partitions the task 3intermediate result (R3) into a plurality of partitions (e.g., R3_1through R3_m). If the task 3 intermediate result is not of sufficientsize to partition, it is not partitioned.

For each partition of the task 3 intermediate result, or for the task 3intermediate results, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes ⅗decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-4, 5, and 7).

FIG. 39 is a diagram of an example of combining result information intofinal results 104 for the example of FIG. 30 . In this example, theresult information includes the list of specific words and/or phrasesfound in the data (task 2 intermediate result), the list of specifictranslated words and/or phrases found in the data (task 3 intermediateresult), the list of non-words found in the data (task 1 firstintermediate result R1-1), the list of unique words found in the data(task 1 second intermediate result R1-2), the list of translation errorsdue to non-words (task 1 sixth intermediate result R1-6), and the listof correctly translated words and/or phrases (task 1 seventhintermediate result R1-7). The task distribution module provides theresult information to the requesting DST client module as the results104.

FIG. 40A is a schematic block diagram of an embodiment of adecentralized agreement module 350 that includes a set of deterministicfunctions 1-N, a set of normalizing functions 1-N, a set of scoringfunctions 1-N, and a ranking function 352. Each of the deterministicfunction, the normalizing function, the scoring function, and theranking function 352, may be implemented utilizing the processing module84 of FIG. 3 . The decentralized agreement module 350 may be implementedutilizing any module and/or unit of a dispersed storage network (DSN).For example, the decentralized agreement module is implemented utilizingthe distributed storage and task (DST) client module 34 of FIG. 1 .

The decentralized agreement module 350 functions to receive a rankedscoring information request 354 and to generate ranked scoringinformation 358 based on the ranked scoring information request 354 andother information. The ranked scoring information request 354 includesone or more of an asset identifier (ID) 356 of an asset associated withthe request, an asset type indicator, one or more location identifiersof locations associated with the DSN, one or more corresponding locationweights, and a requesting entity ID. The asset includes any portion ofdata associated with the DSN including one or more asset types includinga data object, a data record, an encoded data slice, a data segment, aset of encoded data slices, and a plurality of sets of encoded dataslices. As such, the asset ID 356 of the asset includes one or more of adata name, a data record identifier, a source name, a slice name, and aplurality of sets of slice names.

Each location of the DSN includes an aspect of a DSN resource. Examplesof locations includes one or more of a storage unit, a memory device ofthe storage unit, a site, a storage pool of storage units, a pillarindex associated with each encoded data slice of a set of encoded dataslices generated by an information dispersal algorithm (IDA), a DSTclient module 34 of FIG. 1 , a DST processing unit 16 of FIG. 1 , a DSTintegrity processing unit 20 of FIG. 1 , a DSTN managing unit 18 of FIG.1 , a user device 12 of FIG. 1 , and a user device 14 of FIG. 1 .

Each location is associated with a location weight based on one or moreof a resource prioritization of utilization scheme and physicalconfiguration of the DSN. The location weight includes an arbitrary biaswhich adjusts a proportion of selections to an associated location suchthat a probability that an asset will be mapped to that location isequal to the location weight divided by a sum of all location weightsfor all locations of comparison. For example, each storage pool of aplurality of storage pools is associated with a location weight based onstorage capacity. For instance, storage pools with more storage capacityare associated with higher location weights than others. The otherinformation may include a set of location identifiers and a set oflocation weights associated with the set of location identifiers. Forexample, the other information includes location identifiers andlocation weights associated with a set of memory devices of a storageunit when the requesting entity utilizes the decentralized agreementmodule 350 to produce ranked scoring information 358 with regards toselection of a memory device of the set of memory devices for accessinga particular encoded data slice (e.g., where the asset ID includes aslice name of the particular encoded data slice).

The decentralized agreement module 350 outputs substantially identicalranked scoring information for each ranked scoring information requestthat includes substantially identical content of the ranked scoringinformation request. For example, a first requesting entity issues afirst ranked scoring information request to the decentralized agreementmodule 350 and receives first ranked scoring information. A secondrequesting entity issues a second ranked scoring information request tothe decentralized agreement module and receives second ranked scoringinformation. The second ranked scoring information is substantially thesame as the first ranked scoring information when the second rankedscoring information request is substantially the same as the firstranked scoring information request.

As such, two or more requesting entities may utilize the decentralizedagreement module 350 to determine substantially identical ranked scoringinformation. As a specific example, the first requesting entity selectsa first storage pool of a plurality of storage pools for storing a setof encoded data slices utilizing the decentralized agreement module 350and the second requesting entity identifies the first storage pool ofthe plurality of storage pools for retrieving the set of encoded dataslices utilizing the decentralized agreement module 350.

In an example of operation, the decentralized agreement module 350receives the ranked scoring information request 354. Each deterministicfunction performs a deterministic function on a combination and/orconcatenation (e.g., add, append, interleave) of the asset ID 356 of theranked scoring information request 354 and an associated location ID ofthe set of location IDs to produce an interim result. The deterministicfunction includes at least one of a hashing function, a hash-basedmessage authentication code function, a mask generating function, acyclic redundancy code function, hashing module of a number oflocations, consistent hashing, rendezvous hashing, and a spongefunction. As a specific example, deterministic function 2 appends alocation ID 2 of a storage pool 2 to a source name as the asset ID toproduce a combined value and performs the mask generating function onthe combined value to produce interim result 2.

With a set of interim results 1-N, each normalizing function performs anormalizing function on a corresponding interim result to produce acorresponding normalized interim result. The performing of thenormalizing function includes dividing the interim result by a number ofpossible permutations of the output of the deterministic function toproduce the normalized interim result. For example, normalizing function2 performs the normalizing function on the interim result 2 to produce anormalized interim result 2.

With a set of normalized interim results 1-N, each scoring functionperforms a scoring function on a corresponding normalized interim resultto produce a corresponding score. The performing of the scoring functionincludes dividing an associated location weight by a negative log of thenormalized interim result. For example, scoring function 2 divideslocation weight 2 of the storage pool 2 (e.g., associated with locationID 2) by a negative log of the normalized interim result 2 to produce ascore 2.

With a set of scores 1-N, the ranking function 352 performs a rankingfunction on the set of scores 1-N to generate the ranked scoringinformation 358. The ranking function includes rank ordering each scorewith other scores of the set of scores 1-N, where a highest score isranked first. As such, a location associated with the highest score maybe considered a highest priority location for resource utilization(e.g., accessing, storing, retrieving, etc. the given asset of therequest). Having generated the ranked scoring information 358, thedecentralized agreement module 350 outputs the ranked scoringinformation 358 to the requesting entity.

FIG. 40B is a flowchart illustrating an example of selecting a resource.The method begins or continues at step 360 where a processing module(e.g., of a decentralized agreement module) receives a ranked scoringinformation request from a requesting entity with regards to a set ofcandidate resources. For each candidate resource, the method continuesat step 362 where the processing module performs a deterministicfunction on a location identifier (ID) of the candidate resource and anasset ID of the ranked scoring information request to produce an interimresult. As a specific example, the processing module combines the assetID and the location ID of the candidate resource to produce a combinedvalue and performs a hashing function on the combined value to producethe interim result.

For each interim result, the method continues at step 364 where theprocessing module performs a normalizing function on the interim resultto produce a normalized interim result. As a specific example, theprocessing module obtains a permutation value associated with thedeterministic function (e.g., maximum number of permutations of outputof the deterministic function) and divides the interim result by thepermutation value to produce the normalized interim result (e.g., with avalue between 0 and 1).

For each normalized interim result, the method continues at step 366where the processing module performs a scoring function on thenormalized interim result utilizing a location weight associated withthe candidate resource associated with the interim result to produce ascore of a set of scores. As a specific example, the processing moduledivides the location weight by a negative log of the normalized interimresult to produce the score.

The method continues at step 368 where the processing module rank ordersthe set of scores to produce ranked scoring information (e.g., ranking ahighest value first). The method continues at step 370 where theprocessing module outputs the ranked scoring information to therequesting entity. The requesting entity may utilize the ranked scoringinformation to select one location of a plurality of locations.

FIG. 40C is a schematic block diagram of an embodiment of a dispersedstorage network (DSN) that includes the distributed storage and task(DST) processing unit 16 of FIG. 1 , the network 24 of FIG. 1 , and thedistributed storage and task network (DSTN) module 22 of FIG. 1 .Hereafter, the DSTN module 22 may be interchangeably referred to as aDSN memory. The DST processing unit 16 includes a decentralizedagreement module 380 and the DST client module 34 of FIG. 1 . Thedecentralized agreement module 380 be implemented utilizing thedecentralized agreement module 350 of FIG. 40A. The DSTN module 22includes a plurality of DST execution (EX) unit pools 1-P. Each DSTexecution unit pool includes one or more sites 1-S. Each site includesone or more DST execution units 1-N. Each DST execution unit may beassociated with at least one pillar of N pillars associated with aninformation dispersal algorithm (IDA), where a data segment is dispersedstorage error encoded using the IDA to produce one or more sets ofencoded data slices, and where each set includes N encoded data slicesand like encoded data slices (e.g., slice 3′s) of two or more sets ofencoded data slices are included in a common pillar (e.g., pillar 3).Each site may not include every pillar and a given pillar may beimplemented at more than one site. Each DST execution unit includes aplurality of memories 1-M. Each DST execution unit may be implementedutilizing the DST execution unit 36 of FIG. 1 . Hereafter, a DSTexecution unit may be referred to interchangeably as a storage unit anda set of DST execution units may be interchangeably referred to as a setof storage units and/or as a storage unit set.

The DSN functions to receive data access requests 382, select resourcesof at least one DST execution unit pool for data access, utilize theselected DST execution unit pool for the data access, and issue a dataaccess response 392 based on the data access. The selecting of theresources includes utilizing a decentralized agreement function of thedecentralized agreement module 380, where a plurality of locations areranked against each other. The selecting may include selecting onestorage pool of the plurality of storage pools, selecting DST executionunits at various sites of the plurality of sites, selecting a memory ofthe plurality of memories for each DST execution unit, and selectingcombinations of memories, DST execution units, sites, pillars, andstorage pools.

In an example of operation, the DST client module 34 receives the dataaccess request 382 from a requesting entity, where the data accessrequest 382 includes at least one of a store data request, a retrievedata request, a delete data request, a data name, and a requestingentity identifier (ID). Having received the data access request 382, theDST client module 34 determines a DSN address associated with the dataaccess request. The DSN address includes at least one of a source name(e.g., including a vault ID and an object number associated with thedata name), a data segment ID, a set of slice names, a plurality of setsof slice names. The determining includes at least one of generating(e.g., for the store data request) and retrieving (e.g., from a DSNdirectory, from a dispersed hierarchical index) based on the data name(e.g., for the retrieve data request).

Having determined the DSN address, the DST client module 34 selects aplurality of resource levels (e.g., DST EX unit pool, site, DSTexecution unit, pillar, memory) associated with the DSTN module 22. Thedetermining may be based on one or more of the data name, the requestingentity ID, a predetermination, a lookup, a DSN performance indicator,and interpreting an error message. For example, the DST client module 34selects the DST execution unit pool as a first resource level and a setof memory devices of a plurality of memory devices as a second resourcelevel based on a system registry lookup for a vault associated with therequesting entity.

Having selected the plurality of resource levels, the DST client module34, for each resource level, issues a ranked scoring information request384 to the decentralized agreement module 380 utilizing the DSN addressas an asset ID. The decentralized agreement module 380 performs thedecentralized agreement function based on the asset ID (e.g., the DSNaddress), identifiers of locations of the selected resource levels, andlocation weights of the locations to generate ranked scoring information386.

For each resource level, the DST client module 34 receives correspondingranked scoring information 386. Having received the ranked scoringinformation 386, the DST client module 34 identifies one or moreresources associated with the resource level based on the rank scoringinformation 386. For example, the DST client module 34 identifies a DSTexecution unit pool associated with a highest score and identifies a setof memory devices within DST execution units of the identified DSTexecution unit pool with a highest score.

Having identified the one or more resources, the DST client module 34accesses the DSTN module 22 based on the identified one or moreresources associated with each resource level. For example, the DSTclient module 34 issues resource access requests 388 (e.g., write slicerequests when storing data, read slice requests when recovering data) tothe identified DST execution unit pool, where the resource accessrequests 388 further identify the identified set of memory devices.Having accessed the DSTN module 22, the DST client module 34 receivesresource access responses 390 (e.g., write slice responses, read sliceresponses). The DST client module 34 issues the data access response 392based on the received resource access responses 390. For example, theDST client module 34 decodes received encoded data slices to reproducedata and generates the data access response 392 to include thereproduced data.

FIG. 40D is a flowchart illustrating an example of accessing a dispersedstorage network (DSN) memory. The method begins or continues at step 394where a processing module (e.g., of a distributed storage and task (DST)client module) receives a data access request from a requesting entity.The data access request includes one or more of a storage request, aretrieval request, a requesting entity identifier, and a data identifier(ID). The method continues at step 396 where the processing moduledetermines a DSN address associated with the data access request. Forexample, the processing module generates the DSN address for the storagerequest. As another example, the processing module performs a lookup forthe retrieval request based on the data identifier.

The method continues at step 398 where the processing module selects aplurality of resource levels associated with the DSN memory. Theselecting may be based on one or more of a predetermination, a range ofweights associated with available resources, a resource performancelevel, and a resource performance requirement level. For each resourcelevel, the method continues at step 400 where the processing moduledetermines ranked scoring information. For example, the processingmodule issues a ranked scoring information request to a decentralizedagreement module based on the DSN address and receives correspondingranked scoring information for the resource level, where thedecentralized agreement module performs a decentralized agreementprotocol function on the DSN address using the associated resourceidentifiers and resource weights for the resource level to produce theranked scoring information for the resource level.

For each resource level, the method continues at step 402 where theprocessing module selects one or more resources associated with theresource level based on the ranked scoring information. For example, theprocessing module selects a resource associated with a highest scorewhen one resource is required. As another example, the processing moduleselects a plurality of resources associated with highest scores when aplurality of resources are required.

The method continues at step 404 where the processing module accessesthe DSN memory utilizing the selected one or more resources for each ofthe plurality of resource levels. For example, the processing moduleidentifies network addressing information based on the selectedresources including one or more of a storage unit Internet protocoladdress and a memory device identifier, generates a set of encoded dataslice access requests based on the data access request and the DSNaddress, and sends the set of encoded data slice access requests to theDSN memory utilizing the identified network addressing information.

The method continues at step 406 where the processing module issues adata access response to the requesting entity based on one or moreresource access responses from the DSN memory. For example, theprocessing module issues a data storage status indicator when storingdata. As another example, the processing module generates the dataaccess response to include recovered data when retrieving data.

FIG. 41A is a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes the distribute storage andtask (DST) processing unit 16 of FIG. 1 , the network 24 of FIG. 1 , anda plurality of DST execution (EX) unit pools 1-P. The DST processingunit 16 includes a decentralized agreement module 410 and a DST clientmodule 34 of FIG. 1 . The decentralized agreement module 410 may beimplemented utilizing the decentralized agreement module 350 of FIG.40A. Each DST execution unit pool includes a set of n DST executionunits. For example, the DST execution unit pool 1 includes DST executionunits 1-1 through 1-n. Each DST execution unit may be implementedutilizing the DST execution unit 36 of FIG. 1 . Each DST execution unitincludes a plurality of memories 1-M. Each memory may be implementedutilizing the memory 88 of FIG. 3 .

The DSN functions to select a storage pool to provide access to datastored in the selected DST execution unit pool. In an example ofoperation of the selecting, the DST client module 34 receives a dataaccess request 412. The data access request 412 includes at least one ofa store data request, a retrieve data request, a delete data request,and a data object name. Having received the data access request 412, theDST client module 34 determines a DSN address associated with the dataaccess request. The determining includes at least one of generating anew DSN for the store data request and performing a lookup (e.g., in aDSN directory, in an index) based on the data object name for theretrieve data request.

Having determined the DSN address, the DST client module 34 identifies aplurality of available DST execution unit pools of the plurality of DSTexecution unit pools 1-P. The identifying includes at least one ofinterpreting a test result, interpreting an error message, interpretinga query response, and performing a lookup in a storage pool availabilitytable.

For each available DST execution unit pool, the DST client module 34updates a weighting level based on available storage capacity of the DSTexecution unit pool. The updating includes one or more of determiningavailable storage capacity of each DST execution unit of the storagepool, determining the available storage capacity of the storage poolbased on the available storage capacity of each of the DST executionunits of the DST execution unit pool, and recalculating weighting levelsbased on the capacities. For example, the DST client module 34 increasesthe weighting when the capacities are more favorable for further storageof data. As a specific example, the DST client module 34 detects failedmemory devices of the DST execution unit pool 1 and adjusts down theweighting level of the DST execution unit pool 1.

For each DST execution unit pool, the DST client module 34 issues aranked scoring information request 414 to the decentralized agreementmodule 410 for the DSN address based on the weighting level associatedwith the DST execution unit pool. For example, the request includes theDSN address as an asset identifier (ID), a storage pool ID, and thestorage pool weighting level. In response, for each DST execution unitpool, the DST client module 34 receives corresponding ranked scoringinformation 416 from the decentralized agreement module 410.

Having received the ranked scoring information 416, the DST clientmodule 34 selects a DST execution unit pool based on the received rankedscoring information 416. For example, the DST client module 34 selects aDST execution unit pool associated with a highest score. As anotherexample, the DST client module 34 randomly selects a DST execution unitpool from a subset of DST execution unit pools where each DST executionunit pool of the subset of DST execution unit pools is associated with ascore that is greater than a minimum score selection threshold level.Having selected the DST execution unit pool, the DST client module 34issues, via the network 24, resource access requests 418 to the selectedDST execution unit pool for the process the data access request. Forexample, the DST client module 34 issues write slice requests whenstoring data. As another example, the DST client module 34 issues readslice requests when retrieving data.

FIG. 41B is a flowchart illustrating an example of selecting a storagepool. The method includes step 426 where a processing module (e.g., of adistributed storage and task (DST) client module) receives a data accessrequest. The data access request includes one or more of a store datarequest, a retrieve data request, a delete data request, a list datarequest, a data object name, and a data object. The method continues atstep 428 where the processing module determines a dispersed storagenetwork (DSN) address associated with the data access request. Forexample, the processing module generates the DSN address when the dataobject is received. As another example, the processing module performs alookup based on the data object name when the data access requestincludes the retrieve data request.

The method continues at step 430 where the processing module identifiesa plurality of available storage pools. The identifying includes atleast one of interpreting system registry information, interpreting aquery response, performing a lookup, and receiving a list. For eachstorage pool, the method continues at step 432 where the processingmodule updates an associated weighting level based on available storagecapacity of the storage pool. The updating includes at least one ofdetermining available storage capacity of each of a set of storage unitsof the storage pool and determining the associated weighting level basedon the available storage capacity of the storage units.

For each storage pool, the method continues at step 434 where theprocessing module determines ranked scoring information based on the DSNaddress and the updated weighting level. The determining includesperforming a decentralized agreement protocol function on the DSNaddress utilizing the associated weighting level of the storage pool togenerate ranked scoring information for each storage pool.

The method continues at step 436 where the processing module selects astorage pool based on the ranked scoring information. For example, theprocessing module identifies a storage pool associated with a highestranked scoring information level. The method continues at step 438 wherethe processing module further processes the data access requestutilizing the selected storage pool. For example, the processing moduleissues access requests to storage units of the selected storage pool,receives access responses, and processes the access responses togenerate a data access response.

FIG. 42A is a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes the distributed storageand task (DST) processing unit 16 of FIG. 1 , the network 24 of FIG. 1 ,and a set of DST execution (EX) units 1-n. The DST processing unit 16includes the DST client module 34 of FIG. 1 and the memory 88 of FIG. 3. Each DST execution unit may be implemented utilizing the DST executionunit 36 of FIG. 1 .

The DSN functions to select recovery storage resources for recoveringdata from the set of DST execution units, where the data was dispersedstorage error encoded to produce a plurality of sets of encoded dataslices that were stored in the set of DST execution units. In an exampleof operation of the selecting of the recovery storage resources, the DSTclient module 34, when issuing a read slice request 450 to the set ofDST execution units, receives read slice responses 452 from at leastsome of the DST execution units. For example, the DST client module 34receives a read data request 446, generates a set of read slice requests450, sends, via the network 24, the set of read slice requests 1-n tothe set of DST execution units 1-n, and receives the read sliceresponses 452 from at least some of the DST execution units.

Having received the read slice responses 452, the DST client module 34maintains memory ranking information 448 based on the received readslice responses 452. As a specific example, for each read slice request450 to a corresponding DST execution unit, the DST client module 34updates a read ranking number for the DST execution unit, where the readranking number is maintained as a number of received slices from the DSTexecution unit that were successfully utilized to decode data divided bya total number of corresponding read requests issued to the DSTexecution unit. Having updated the read ranking number, the DST clientmodule 34 updates the memory ranking information 448 stored in thememory 88 with the updated read ranking numbers.

Having maintained in the memory ranking information, the DST clientmodule 34 receives another read data request 446 to recover data fromthe set of DST execution units. Having received the read data request446, the DST client module 34 selects a read threshold number of DSTexecution units of the set of DST execution units based on the memoryranking information 448 for the set of DST execution units. Theselecting includes one or more of identifying a read threshold number ofDST execution units associated with highest read ranking numbers andidentifying a read threshold number of DST execution units associatedwith read ranking numbers greater than a minimum ranking thresholdlevel.

Having selected the read threshold number of DST execution units, theDST client module 34 recovers the data from the selected read thresholdnumber of DST execution units. As an example of the recovering, the DSTclient module 34 issues, via the network 24, read slice requests 450 tothe selected read threshold number of DST execution units, receives readslice responses 452, and for each set of encoded data slices, dispersedstorage error decodes a decode threshold number of received encoded dataslices to reproduce a data segment, and aggregates a plurality ofrecovered data segments to produce recovered data. Having recovered thedata, the DST client module 34 outputs a read data response 454 thatincludes the recovered data.

FIG. 42B is a flowchart illustrating an example of selecting recoverystorage resources. The method includes step 460 where a processingmodule (e.g., of a distributed storage and task (DST) client module)maintains memory ranking information for a set of storage units based onhistorical utilization of received encoded data slices from the set ofstorage units. For example, the processing module issues read slicerequests to the set of storage units, receives read slice responses fromat least some of the storage units, utilizes encoded data slicesextracted from the received read slice responses, updates the memoryranking information by determining, for each storage unit, a readranking number. The determining of the read ranking number includesperforming a mathematical function to normalize a number of successfullyutilized encoded data slices divided by a corresponding number of readslice requests issued to the corresponding storage unit.

The method continues at step 462 where the processing module receives aread data request to recover data from the set of storage units. Theread data request includes an identifier of the data, where the data wasdispersed storage error encoded to produce a plurality of sets ofencoded data slices for storage in the set of storage units.

The method continues at step 464 where the processing module selects aread threshold number of storage units of the set of storage units basedon the memory ranking information. For example, the processing moduleidentifies a read threshold number of storage units with highest readranking numbers. As another example, the processing module identifies aread threshold number of storage units associated with read rankingnumbers greater than a minimum ranking threshold level.

The method continues at step 466 where the processing module recoversthe data from the selected storage units. For example, the processingmodule issues read slice requests to the selected read threshold numberof storage units, receives encoded data slices, and for each set ofencoded data slices, dispersed storage error decodes a decode thresholdnumber of received encoded data slices to produce a recovered datasegment, updates historical records for successfully utilization of eachencoded data slice, and aggregates a plurality of recovered datasegments to produce recovered data.

The method continues at step 468 where the processing module outputs aread data response to a requesting entity. The outputting includesgenerating the read data response to include one or more of therecovered data, the identifier of the data and sending the read dataresponse to the requesting entity.

FIG. 43A is a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes the distributed storageand task (DST) processing unit 16 of FIG. 1 , the network 24 of FIG. 1 ,and a DST execution (EX) unit pool 476. The DST processing unit 16includes the DST client module 34 of FIG. 1 . The DST execution unitpool 476 includes a set of DST execution units 1-n. Each DST executionunit includes a set of M memories. Each memory may be implementedutilizing the memory 88 of FIG. 3 .

The DSN functions to select a data integrity maintenance process formaintaining integrity of stored data within the DST execution unit pool.In an example of operation of the selecting of the data integritymaintenance process, the DST client module 34 detects one or morefailing memory devices of a set of memory devices associated with a DSNaddress range, where a plurality of DSN address ranges 1-M areassociated with the set of DST execution units. The detecting includesat least one of interpreting a received error message, initiating aquery, receiving a query response, initiating a memory test, andinterpreting a memory test result.

The plurality of DSN address ranges 1-M includes the DSN address range,where a data object is stored as a plurality of sets of encoded dataslices in at least one set of memory devices. The DST processing unit 16divides the data object into a plurality of data segments where eachdata segment is dispersed storage error encoded to produce a set ofencoded data slices for storage in a corresponding set of memorydevices. For example, the DST client module 34 receives error messagesindicating that memories 1-2 and 2-2 are failing of the set of memorydevices associated with the DSN address range 2.

For a data segment of the plurality of data segments, the DST clientmodule 34 determines a number of available encoded data slices of acorresponding set of encoded data slices stored in a corresponding setof memory devices. The determining includes at least one of interpretinga list slice responses, interpreting read slice responses, andinterpreting one or more error messages.

When the number of available encoded data slices compares unfavorably toa rebuilding threshold level (e.g., less than the rebuilding thresholdlevel), the DST process client module 34 initiates a data salvagingprocess to recover encoded data slices of the set of encoded data slicesfrom the corresponding failing memory devices. For example, the DSTclient module 34 issues, via the network 24, urgent read slice requests480 for the encoded data slices to be recovered to the DST executionunits corresponding to the failing memory devices. For instance, the DSTclient module 34 issues, via the network 24, the urgent read slicerequests 480 to DST execution units 1 and 2 to quickly recoverrecoverable encoded data slices from the memories 1-2, and 2-2. DSTexecution units receiving a corresponding urgent read slice requestprioritizes issuing a corresponding urgent read slice response 482. Theprioritizing includes one or more of de-prioritizing one or more ofother access requests and maintenance tasks and raising priority ofexecution of tasks related to issuing the urgent read slice response(e.g., DST execution unit 1 immediately accesses the memory 1-2 torecover encoded data slices prior to a complete failure of the memory1-2).

Having issued the urgent read slice request 480, the DST client module34 receives, via the network 24, urgent read slice responses 482. Havingreceived the urgent read slice responses 482, the DST client module 34facilitates temporary storage of encoded data slices extracted from thereceived urgent read slice responses 482. For example, the DST clientmodule 34 sends the extracted encoded data slices to another DSTexecution unit for temporary storage.

FIG. 43B is a flowchart illustrating an example of selecting a dataintegrity maintenance process. The method includes step 486 where aprocessing module (e.g., of a distributed storage and task (DST) clientmodule) detects one or more failing memory devices of a set of memorydevices, where a data segment is dispersed storage error encoded toproduce a set of encoded data slices that are stored in the set ofmemory devices. The detecting includes one or more of interpreting anerror message, interpreting a query response, interpreting a read sliceresponse, correlating identified failed memory devices with a common setof memory devices.

The method continues at step 488 where the processing module determinesa number of available encoded data slices of the set of encoded dataslices. The determining includes at least one of identifying a dataobject associated with storage of the set of encoded data slices in theset of memory devices, interpreting list slice responses, interpretingread slice responses, and interpreting error messages.

When the number of available encoded data slices compares unfavorably toa rebuilding threshold level, the method continues at step 490 where theprocessing module issues one or more urgent read slice requests tostorage units associated with at least some of the one or more failingmemory devices to recover available encoded data slices associated withthe at least some of the feeling memory devices. The issuing includes atleast one of determining that the number of available encoded dataslices is less than the rebuilding threshold level, and determining thatthe number of available encoded data slices is substantially the same asat least one of a decode threshold number and a read threshold number.The issuing further includes identifying the storage units associatedwith the failing memory devices associated with a set of encoded dataslices, generating the one or more urgent read slice request, andsending the generated one or more urgent read slice requests to theidentified storage units.

The method continues at step 492 where the processing module receivesone or more urgent read slice responses that includes one or moreencoded data slices. The method continues at step 494 where theprocessing module facilitates temporary storage of encoded data slicesextracted from the received urgent read slice responses. For example,the processing module issues a write slice request to a storage unitassociated with an available memory device to store the extractedencoded data slices, where the write slice request includes theextracted encoded data slices.

FIG. 44A is a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes the distributed storageand task (DST) processing unit 16 of FIG. 1 , the network 24 of FIG. 1 ,and a set of DST execution (EX) units 1-n. The DST processing unit 16includes the DST client module 34 of FIG. 1 and the memory 88 of FIG. 3. Each DST execution unit may be implemented utilizing the DST executionunit 36 of FIG. 1 .

The DSN functions to improve a data retrieval reliability level withregards to retrieving data stored in the set of DST execution units 1-n.In an example of operation of the improving of the data retrievalreliability level, the DST client module 34 maintains DSN loadinginformation 502 while processing data access requests 500 (e.g., storedata request, retrieve data requests) and facilitating execution ofmaintenance tasks (e.g., scanning for storage errors, rebuilding dataassociated with the detected storage errors). For example, the DSTclient module 34 tracks rates of data access and maintenance tasksversus time and stores the DSN loading information 502 in the memory 88.

While maintaining the DSN loading information 502, the DST client module34 estimates a future data access task rate. The estimating may be basedon one or more of the DSN loading information 502, interpreting a taskqueue, obtaining current loading rates, interpreting a schedule, andinterpreting a message. Having estimated the future data access taskrate, the DST client module 34 determines a probability of future dataloss based on the estimated future data access task rate. Thedetermining includes at least one of estimating future capacity forexecution of maintenance tasks based on the estimated future data accessrate (e.g., during expected future access peaks) and estimating impacton data retrieval reliability levels based on the estimated futurecapacity for execution of maintenance tasks.

Having determined the probability of potential future data loss, the DSTclient module 34, when the probability of potential future data losscompares unfavorably to a maximum probability of data loss thresholdlevel and while a current data access task rate is less than a maximumrate level, facilitates execution of a preventative data loss mitigationprocess. The facilitating includes at least one of updating dispersalparameters to include a larger write threshold number to minimizeaddition of future rebuilding tasks, accelerating schedule maintenancetasks, raising a priority level of execution of maintenance tasks, andtemporarily de-prioritizing priority levels of execution of data accesstasks (e.g., receive data access requests 500, issue slice accessrequests 504, receive slice access responses 506, and issue data accessresponses 508).

When the current data access task rate is greater than the maximum ratelevel (e.g., detecting a peak loading time frame of data accessrequests), the DST client module 34 suspends execution of thepreventative data loss mitigation process. The facilitating includes oneor more of resuming use of a previous set of dispersal parameters toinclude a smaller read threshold number to minimize data access delays,de-accelerating scheduled maintenance tasks, lowering a priority levelof execution of maintenance task, and temporarily prioritizing prioritylevels of execution of data access tasks.

FIG. 44B is a flowchart illustrating an example of improving a dataretrieval reliability level. The method includes step 516 where aprocessing module (e.g., of a distributed storage and task (DST) clientmodule) maintains dispersed storage network (DSN) loading information.For example, the processing module stores rates of data access andmaintenance tasks versus time in a local memory. The method continues atstep 518 where the processing module estimates a future data access taskrate. The estimating includes generating the future data access taskrate based on one or more of the DSN loading information, interpreting atask queue, interpreting current loading rates, interpreting a schedule,and interpreting the message.

The method continues at step 520 where the processing module determinesa probability level of potential future data loss based on the estimatedfuture data access task rate. The determining includes at least one ofestimating a future capacity for execution of maintenance tasks based onthe estimated future data access rate and estimating a data retrievalreliability level based on the estimated future capacity for executionof maintenance tasks.

When the probability level of the potential future data loss comparesunfavorably to a maximum probability of data loss threshold level, themethod continues at step 522 where the processing module facilitatesexecution of a preventative data loss mitigation process. Thefacilitating includes one or more of storing more slices of each set ofnewly stored encoded data slices (e.g., increase a write thresholdnumber), prioritizing maintenance tasks, and a de-prioritizing dataaccess tasks.

When a current data access task rate is greater than a maximum task ratelevel, the method continues at step 524 where the processing modulesuspends the execution of the preventative data loss mitigation process.The suspending includes one or more of storing fewer slices of each setof newly stored encoded data slices (e.g., decreasing the read thresholdnumber), de-prioritizing maintenance task, and prioritizing data accesstasks.

FIG. 45A is a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes the distributed storageand task (DST) processing unit 16 of FIG. 1 , the network 24 of FIG. 1 ,an active DST execution (EX) unit pool 530 and a standby DST executionunit pool 532. The DST processing unit 16 includes the DST client module34 of FIG. 1 . Each of the active and standby DST execution unit pools530-532 includes a set of DST execution units. For example, the activeDST execution unit pool 530 includes DST execution units A1 through AMand the standby DST execution unit pool 532 includes DST execution unitsS1 through SN, where the active DST execution unit pool 530 includes MDST execution units and the standby DST execution unit pool 532 includesN DST execution units. Each DST execution unit may be implementedutilizing the DST execution unit 36 of FIG. 1 .

The DSN functions to utilize storage resources for accessing data storedin at least one of the active DST execution unit pool 530 and thestandby DST execution unit pool 532. In an example of operation of theutilizing of the storage resources, the DST client module 34 determinesa failure rate of DST execution units of the active DST execution unitpool 530. The determining includes one or more of tracking how many DSTexecution units fail per day, identifying a meantime to repair, andcalculating a rate of failed units per day = failing units per daymultiplied by days to repair.

Having determined the failure rate, the DST client module 34 establishesa number of standby DST execution units of the standby DST executionunit pool 532 based on the determined failure rate of the DST executionunits of the active DST execution unit pool. The establishing includesone or more of issuing an alert, commissioning a dormant DST executionunit, enabling a disabled DST execution unit, and repurposing anavailable standby DST execution unit.

When detecting a failed DST execution unit of the active DST executionunit pool, the DST client module 34 identifies an associated DSN addressrange of the failed DST execution unit. The identifying includes atleast one of receiving the DSN address range, initiating a query,interpreting a query response, and performing a lookup. Havingidentified the associated DSN address range, the DST client module 34selects an available standby DST execution unit. The selecting may bebased on one or more of an availability level, a storage capacity level,a round-robin selection scheme, and utilization of a distributedagreement protocol function (e.g., a highest ranked DST execution unitis selected).

Having selected the standby DST execution unit, the DST client module 34facilitates populating the selected standby DST execution unit with oneor more encoded data slices associated with the failed DST executionunit based on the associated DSN address range. The facilitatingincludes at least one of rebuilding encoded data slices of theassociated DSN address range, storing rebuilt encoded data slices in thestandby DST execution unit, and updating slice location information(e.g., of a DSN directory, of an index) to associate the DSN addressrange with the selected standby DST execution unit and to disassociatethe DSN address range with the failed DST execution unit.

Having facilitated the populating of the selected standby DST executionunit, the DST client module 34 facilitates processing of the receiveddata access requests 534 for data associated with the DSN address rangeby utilizing the selected standby DST execution unit. For example, theDST client module 34 communicates standby slice access messages 540 withthe standby DST execution unit.

When detecting availability of the failed DST execution unit, the DSTclient module 34 facilitates reactivating the now available andpreviously failed DST execution unit. For example, the DST client module34 transfers (e.g., via standby slice access messages 540) the rebuiltencoded data slices from the selected standby DST execution unit to thenow available DST execution unit (e.g., via active slice access messages538), associates the DSN address range with the now available DSTexecution unit, and disassociates the DSN address range with theselected standby DST execution unit.

Having reactivated the now available and previously failed DST executionunit, the DST client module 34 facilitates processing of further receivedata access requests 534 for the data associated with the DSN addressrange by utilizing the now available DST execution unit. For example,the DST client module 34 communicates slice access requests 536 asactive slice access messages with the now available DST execution unit,and receives slice access responses 542.

FIG. 45B is a flowchart illustrating an example of utilizing storageresources. The method includes step 550 where a processing module (e.g.,of a distributed storage and task (DST) client module) determines afailure rate of storage units of an active storage unit pool. Thedetermining includes identifying unit failures per day and mean time torepair. The method continues at step 552 where the processing moduleestablishes a number of standby storage units. For example, theprocessing module activates a number of standby storage units inaccordance with the failure rate.

When detecting a failed storage unit of the active storage unit pool,the method continues at step 554 where the processing module identifiesan associated DSN address range. The identifying includes at least oneof performing a lookup, interpreting an error message, and interpretinga query response. The method continues at step 556 where the processingmodule selects an available standby storage unit. The selecting may bebased on one or more of an availability level, a storage capacity level,a list, and a distributed agreement protocol function output.

The method continues at step 558 where the processing module facilitatespopulating the selected standby storage unit with one or more encodeddata slices associated with the failed storage unit based on theassociated DSN address range. For example, the processing modulerebuilds the encoded data slices of the DSN address range associatedwith the failed storage unit, stores the rebuilt encoded data slices inthe standby storage unit, associates the DSN address range with thestandby storage unit, and disassociates the DSN address range with thefailed storage unit.

The method continues at step 560 where the processing module facilitatesprocessing the received data access requests for data associated withthe DSN address range by utilizing the selected standby storage unit.For example, the processing module attempts to recover encoded dataslices of the DSN address range from the standby storage unit.

When detecting activation of a replacement storage unit for the failedstorage unit, the method continues at step 562 where the processingmodule facilitates populating the replacement storage unit with one ormore encoded data slices from the selected standby storage unit, wherethe one or more encoded data slices are associated with the DSN addressrange. The replacement storage unit may include the failed storage unitwhen the failed storage unit has been repaired. In an example of thefacilitating, the processing module transfers all encoded data slices ofthe DSN address range from the standby storage unit to the replacementstorage unit.

The method continues at step 564 where the processing module facilitatesprocessing of further receive data access requests for data associatedwith the DSN address range by utilizing the replacement storage unit.For example, the processing module attempts to recover encoded dataslices of the DSN address range from the replacement storage unit.

FIGS. 46A and 46B are schematic block diagrams of another embodiment ofa dispersed storage network (DSN) that includes a distributed storageand task (DST) processing unit A, a DST processing unit B, and a set ofDST execution units 1-5. Each DST processing unit may be implementedutilizing the DST processing unit 16 of FIG. 1 . Each DST execution unitmay be implemented utilizing the DST execution unit 36 of FIG. 1 .Hereafter, the DST processing units may be interchangeably referred toas computing devices, the set of DST execution units 1-5 may beinterchangeably referred to as a set of storage units, and each DSTexecution unit may be interchangeably referred to as a storage unit. TheDST processing units A-B and the set of DST execution units 1-5 may beoperably coupled utilizing the network 24 of FIG. 1 . The DSN functionsto store data in the set of DST execution units.

FIG. 46A illustrates steps of an example of operation of the storing ofthe data where the set of storage units receives a plurality of sets ofnon-locking write requests from a plurality of computing devices, whereeach set of non-locking write requests includes a set of encoded dataslices and a set of slice names, where, from set to set of non-lockingwrite requests, the set of slices names are substantially identical.Each non-locking write request includes at least one of an instructionto store an encoded data slice associated with the non-locking writerequest and an instruction to keep the stored encoded data slice hiddenuntil a write finalize command is received for the stored encoded dataslice. For example, DST processing units A and B each issue (e.g.,substantially simultaneously) a set of commit immediate requests to theset of DST execution units, where each set of commit immediate requestsincludes the set of encoded data slices, the (common) set of slicenames, a revision level of the corresponding set of encoded data slices,and an expected revision level for a latest revision level of a set ofstored encoded data slices corresponding to the set of slice names. Forexample, DST processing unit A issues commit immediate requests forslices (SLC) A-1 through A-5 to the set of DST execution units 1-5 andDST processing unit B issues commit immediate requests for slices B-1through B-5 to the set of DST execution units 1-5, where the set ofslice names are substantially identical for the set of encoded dataslices A-1 through A-5 and for the set of encoded data slices B-1through B-5.

Each storage unit of the set of storage units stores an encoded dataslice of a respective one of the non-locking write requests of each ofthe plurality of sets of non-locking write requests. For example, theDST execution unit 1 stores the received encoded data slice A-1 of theset of encoded data slices A-1 through A-5 and stores the receivedencoded data slice B-1 of the set of encoded data slices B-1 through B-5when receiving the encoded data slice A-1 and the encoded data slice B-1within a storage operation time frame (e.g., prior to receiving anotherassociated request including a finalize request, prior to a storageoperation time frame expiring).

Having stored the encoded data slice, each storage unit sends a writeresponse regarding the respective one of the non-locking write requestsof each of the plurality of sets of non-locking write requests toproduce, per storage unit, a group of write responses and where, eachwrite response in the group of write responses includes an orderingindication. The ordering indication includes at least a relativeordering of write requests corresponding to the group of write responsesbased on one or more ordering approaches. A first ordering approachincludes selecting a random relative ordering. A second orderingapproach includes prioritizing requests of a particular DST processingunit.

A third ordering approach includes generating the ordering indication toindicate whether an associated write request of the write response was afirst received write request (e.g., not conflicting) of the non-lockingwrite requests for not the first received write request (e.g.,potentially conflicting). For example, the DST execution unit 1 sends awrite response to the DST processing unit A indicating that the encodeddata slice A-1 was not received first amongst the non-locking writerequests and sends another write response to the DST processing unit Bindicating that the encoded data slice B-1 was received first amongstthe non-locking write requests when the DST execution 1 receives theencoded data slice B-1 ahead of encoded data slice A-1. As anotherexample, the DST execution unit 2 sends a write response to the DSTprocessing unit A indicating that the encoded data slice A-2 wasreceived first amongst the non-locking write requests received by theDST execution unit 2 and sends another write response to the DSTprocessing unit B indicating that the encoded data slice B-2 was notreceived first amongst the non-locking write requests received by theDST execution unit 2 when the DST execution 2 receives the encoded dataslice A-2 ahead of encoded data slice B-2.

A computing device of the plurality of computing devices receives a setof write responses from the set of storage units regarding acorresponding one of the plurality of sets of non-locking writerequests. For example, the DST processing unit A commit immediateresponses for slices A-1 through A-5 from the DST execution units 1-5with regards to the set of commit immediate requests for slices A-1through A-5.

Having received the set of write responses, the computing devicedetermines whether a threshold number of write responses of the set ofwrite responses has an expected ordering indication. The thresholdnumber may include a write threshold number, where the write thresholdnumber is equal to or greater than a decode threshold number (e.g., 3)and less than a width number (e.g., 5). For example, the thresholdnumber is 4 when the write threshold is utilized and the width number is5 and the decode threshold number is 3. The expected ordering indicationincludes the indication that the write requests corresponding to thewrite response was received first amongst the plurality of writerequests received by a corresponding storage unit. For example, the DSTprocessing unit A indicates that the threshold number of write responsesof the set of write responses has the expected ordering indication whenthe commit immediate response for slice A-1 indicates that the encodeddata slice A-1 was not received first (e.g., encoded data slice B-1 wasreceived first by the DST execution unit 1), the commit immediateresponses for slices A-2 and through A-5 all indicate that encoded dataslices A-2 through A-5 received first (e.g., ahead of the encoded dataslices B-1 through B-5), and the write threshold is 4.

Alternatively the computing device determines whether the thresholdnumber of write responses of the set of write responses has the expectedordering indication of utilizing a revision level approach. The revisionlevel approach includes the computing device determining, in accordancewith a storage protocol of the DSN, an expected revision level for theset of slices names of the corresponding one of the plurality of sets ofnon-locking write requests, where the ordering indication corresponds toa revision level, comparing the expected revision level with therevision level included in each write response of the set of writeresponses, and when the threshold number of write responses has theexpected revision level, indicating that the threshold number of writeresponses has the expected ordering indication.

FIG. 46B illustrates further steps of the example of operation of thestoring of the data where, the threshold number of write responses hasthe expected ordering indication, the computing device sends a set ofwrite finalize requests to the set of storage units. Alternatively, whenthe threshold number of write responses does not have the expectedordering indication, the computing device issues an undo command to theset of storage units to facilitate deletion of the corresponding set ofencoded data slices. As an example of sending the set of write finalizerequests, the DST processing unit A, when determining that the thresholdnumber of write responses has the expected ordering indication (e.g., atleast a write threshold number of encoded data slices of the set ofencoded data slices A-1 through A-5 have been stored first), issues aset of finalize requests for encoded data slices A-1 through A-5 to theset of DST execution units 1-5.

The set of storage units finalizes storing the set of encoded dataslices corresponding to the corresponding one of the plurality of setsof non-locking write requests regardless of the ordering indication ofthe set of write responses, where the finalizing the storing includesdeleting another set of encoded data slices corresponding to another oneof the plurality of sets of non-locking write requests. For example, theDST execution unit 1 deletes the temporarily stored encoded data sliceB-1 even though encoded data slice B-1 was received ahead of the encodeddata slice A-1, and the DST execution units 2-5 deletes the temporarilystored encoded data slices B-2 through B-5 (e.g., which were receivedafter encoded data slices A-2 through A-5).

As another example of the finalizing of the storing, a storage unit ofthe set of storage units determines a revision level for each respectiveone of the non-locking write requests of each of the plurality of setsof non-locking write requests, associates the revision levels withslices names of the encoded data slices of associated with eachrespective one of the non-locking write requests of each of theplurality of sets of non-locking write requests to includes arepresentation of the revision levels, and the storage units stores theencoded data slices based on the slices names and the revision levels.For example, the DST execution units 2-5 stores the encoded data slicesA-2 through A-5 (e.g., rather than temporarily stores) and deletes thetemporarily stored encoded data slices B-2 through B-5 when the encodeddata slices A-2 through A-5 were temporarily stored by the DST executionunits 2-5 with an association of a preferred next revision level (e.g.,an incremental revision level compared to a previously stored set ofencoded data slices prior to receiving the non-locking write requests.

Alternatively, or in addition to the set of storage units receives aplurality of sets of locking write requests from the plurality ofcomputing devices, where each set of locking write requests (e.g., writerequests rather than commit immediate write requests) includes a secondset of encoded data slices and a second set of slice names, where, fromset to set of locking write requests, the second set of slices names aresubstantially identical. Each storage unit of the set of storage unitsstores an encoded data slice from one of the plurality of sets oflocking write requests based on an ordering of receiving correspondingwrite requests from each of the plurality of sets of locking writerequests, sends a locking write response regarding the respective storedencoded data slice of the second set of encoded data slices to acorresponding one of the plurality of computing devices, where thecomputing device receives a set of locking write responses from the setof storage units regarding a corresponding one of the plurality of setsof locking write requests. When the set of locking write responsesincludes at least the threshold number, the computing device sends writecommit requests to storage units of the set of storage units thatprovided one of the set of locking write responses.

FIG. 46C is a flowchart illustrating an example of storing data. Inparticular, a method is presented for use in conjunction with one ormore functions and features described in conjunction with FIGS. 1-39,46A-B, and also FIG. 46C. The method includes step 576 where a set ofstorage units of a dispersed storage network (DSN) receives a pluralityof sets of non-locking write requests from a plurality of computingdevices, where each set of non-locking write requests includes a set ofencoded data slices and a set of slice names, where, from set to set ofnon-locking write requests, the set of slices names are substantiallyidentical.

The method continues at step 578 where each storage unit of the set ofstorage units stores an encoded data slice of a respective one of thenon-locking write requests of each of the plurality of sets ofnon-locking write requests. The method continues at step 580 where eachstorage unit of the set of storage units sends a write responseregarding the respective one of the non-locking write requests of eachof the plurality of sets of non-locking write requests to produce, perstorage unit, a group of write responses and where, each write responsein the group of write responses includes an ordering indication.

The method continues at step 582 where a computing device of theplurality of computing devices receives a set of write responses fromthe set of storage units regarding a corresponding one of the pluralityof sets of non-locking write requests. The method continues at step 584where the computing device determines whether a threshold number ofwrite responses of the set of write responses has an expected orderingindication. For example, the computing device determines, in accordancewith a storage protocol of the DSN, an expected revision level for theset of slices names of the corresponding one of the plurality of sets ofnon-locking write requests, where the ordering indication corresponds toa revision level, compares the expected revision level with the revisionlevel included in each write response of the set of write responses, andwhen the threshold number of write responses has the expected revisionlevel, the computing device indicates that the threshold number of writeresponses has the expected ordering indication.

When the threshold number of write responses has the expected orderingindication, the method continues at step 586 where the computing devicesends a set of write finalize requests to the set of storage units.Alternatively, when the threshold number of write responses does nothave the expected ordering indication, the computing device issues anundo command to the set of storage units to facilitate deleting encodeddata slices associated with write responses that do not have expectedordering information.

The method continues at step 588 where the set of storage unitsfinalizes storage of the set of encoded data slices corresponding to thecorresponding one of the plurality of sets of non-locking write requestsregardless of the ordering indication of the set of write responses. Forexample, a storage unit of the set of storage units determines arevision level for each respective one of the non-locking write requestsof each of the plurality of sets of non-locking write requests,associates the revision levels with slices names of the encoded dataslices of associated with each respective one of the non-locking writerequests of each of the plurality of sets of non-locking write requeststo includes a representation of the revision levels, and the storageunits store the encoded data slices based on the slices names and therevision levels. The method continues at step 590 where the set ofstorage units deletes another set of encoded data slices correspondingto another one of the plurality of sets of non-locking write requests.

Alternatively, or in addition to, the set of storage units receives aplurality of sets of locking write requests from the plurality ofcomputing devices, where each set of locking write requests includes asecond set of encoded data slices and a second set of slice names,where, from set to set of locking write requests, the second set ofslices names are substantially identical. Each storage unit of the setof storage units stores an encoded data slice from one of the pluralityof sets of locking write requests based on an ordering of receivingcorresponding write requests from each of the plurality of sets oflocking write requests and sends a locking write response regarding therespective stored encoded data slice of the second set of encoded dataslices to a corresponding one of the plurality of computing devices. Thecomputing device receives a set of locking write responses from the setof storage units regarding a corresponding one of the plurality of setsof locking write requests and when the set of locking write responsesincludes at least the threshold number, the computing device sends writecommit responses to storage units of the set of storage units thatprovided one of the set of locking write responses.

The method described above in conjunction with one or more of thecomputing devices and the storage units can alternatively be performedby one or more processing modules or other modules of the dispersedstorage network or by other devices. In addition, at least one memorysection (e.g., a non-transitory computer readable storage medium) thatstores operational instructions can, when executed by one or moreprocessing modules of one or more computing devices and/or storage unitsof the dispersed storage network (DSN), cause the one or more computingdevices and/or storage units to perform any or all of the method stepsdescribed above.

FIG. 47A is a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes the distributed storageand task (DST) processing unit 16 of FIG. 1 , the DST integrityprocessing unit 20 of FIG. 1 , the network 24 of FIG. 1 , and a set ofDST execution (EX) units 1-n. The DST processing unit 16 includes theDST client module 34 of FIG. 1 and the memory 88 of FIG. 3 . The DSTintegrity processing unit 20 includes the processing module 84 of FIG. 3and the memory 88 of FIG. 3 . Each DST execution unit may be implementedutilizing the DST execution unit 36 of FIG. 1 .

The DSN functions to maintain integrity of stored data where a largedata object is stored as a plurality of sets of encoded data slices inthe set of DST execution units. In an example of operation of themaintaining of the integrity of the stored data, the DST client module34 determines to store a large data object in the set of DST executionunits. For example, the DST client module 34 receives a store datarequest 600 that includes one or more of a portion of the large dataobject, a size indicator of large data object, and an identifier of thelarge data object.

Having determined to store the large data object, the DST client module34 generates write-intent information 602 based on one or more of thelarge data object and system storage capability information (e.g., asystem performance levels, system capacities, network performance level,available storage, available processing capabilities). For example, theDST client module 34 generates the write-intent information 602 toinclude one or more of the size indicator of the large data object, anumber of regions of the large data object, a region size indicator, anumber of data segments per region, a slice name range, a source nameassociated with the identifier of the large data object, the identifierof the large data object, an identifier of a requesting entity, acurrent timestamp, and an estimated time to completion of the storing.

Having generated the write-intent information 602, the DST client module34 stores the write-intent information 602 in the set of DST executionunits. For example, the DST client module 34 dispersed storage errorencodes the write-intent information 602 to create a set of write-intentslices 604 and sends, via the network 24, the set of write-intent slices604 to the set of DST execution units for storage. The storing mayfurther include maintaining a local copy of the write-intent information602 within the memory 88 of the DST processing unit 16.

Having stored the write-intent information 602, the DST client module 34divides the large data object into one or more regions (e.g., asserially received), and for each region, divides the region into aplurality of data segments, and for each data segment, dispersed storageerror encodes the data segment to produce a set of encoded data slices,sends the network 24, each set of encoded data slices 606 to the set ofDST execution units for storage (e.g., issues write slice requests asslice access requests 1-n), and receives write slice responses 608 withregards to the storing of the encoded data slices 606.

While storing the large data object, the DST client module 34 updatesthe stored write-intent information 602. For example, the DST clientmodule 34 recovers the write-intent information 602 from the set of DSTexecution units (e.g., obtains a decode threshold number of write-intentslices 604, dispersed storage error decodes the decode threshold numberof write-intent slices to reproduce the write-intent information 602),updates the timestamp with an updated current timestamp, and stores theupdated write-intent information 602 in the set of DST execution units.

While the large data object is being stored, the processing module 84recovers the write-intent information 602 from the set of DST executionunits. For example, the processing module 84 issues read slice requestsas slice access requests to the set of DST execution units, receivesread slice responses, extracts the write-intent slices 604 from the readslice responses, and dispersed storage error decodes a decode thresholdnumber of extracted write-intent slices to reproduce the write-intentinformation 602. Having reproduced the write-intent information 602, theprocessing module 84 stores the write-intent information 602 in thememory 88 of the DST integrity processing unit 20.

Having recovered the write-intent information 602, the processing module84 determines whether the storing of the large data object has stalledand/or failed. For example, the processing module 84 indicates that thestoring has stalled when a difference between a timestamp of thewrite-intent information 602 and a current timestamp is greater than atimestamp threshold level. As another example, the processing module 84indicates that the storing has stalled when detecting that a writethreshold number of write locks does not exist when write locks areutilized. As yet another example, the processing module 84 interprets areceived error message indicating that the storing of the large dataobject has stalled and/or failed.

When the storing of the large data object has stalled, the processingmodule 84 initiates deleting of one or more portions of the large dataobject from the set of DST execution units. For example, the processingmodule 84 issues, via the network 24, delete slice requests 610 to theset of DST execution units for the slice name range of the large dataobject to the set of DST execution units and issues delete slicerequests 610 to delete the write-intent information 602 from the set ofDST execution units.

FIG. 47B is a flowchart illustrating an example of maintaining integrityof stored data. The method includes step 616 where a processing moduledetermines to store a large data object in a set of storage units. Forexample, the processing module receives a store data request. As anotherexample, the processing unit identifies a failed previous attempt tostore the large data object. The method continues at step 618 where theprocessing module generates write-intent information. The methodcontinues at step 620 where the processing module stores thewrite-intent information in the set of storage units. For example, theprocessing unit dispersed storage error encodes the write-intentinformation to produce a set of write-intent slices, and sends the setof write-intent slices to the set of storage units for storage.

The method continues at step 622 where the processing module initiates astorage of the large data object in the set of storage units. Forexample, the processing module encodes portions of the large data objectto produce encoded data slices and sends the encoded data slices to theset of storage units for storage. While storing large data object, themethod continues at step 624 where the processing module updates thestored write-intent information to indicate that the storing has notstalled. For example, the processing module determines that an updatetime frame has expired and updates the current timestamp of the storedwrite-intent information.

The method continues at step 626 where an integrity unit recovers thewrite-intent information from the set of storage units. For example, theintegrity unit obtains the write-intent slices from the set of storageunits and dispersed storage error decodes the obtained write-intentslices to reproduce the write-intent information. The method continuesat step 628 where the integrity unit determines whether the storing ofthe large data object has stalled. For example, the integrity unitindicates that the storing has stalled when a difference between arecovered timestamp of the recovered write-intent information and acurrent timestamp is greater than a timestamp threshold level. Asanother example, the integrity unit interprets a received error message.

When the storing of the large data object has stalled, the methodcontinues at step 630 where the integrity unit initiates deletion ofstored portions of the large data object. For example, the integrityunit issues delete slice requests for a slice name range of the largedata object to the set of storage units and issues delete slice requeststo delete the set of write-intent slices from the set of storage units.

FIG. 48A is a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes a set of distributedstorage and task (DST) execution units 1-n, the network 24 of FIG. 1 ,and the DST integrity processing unit 20 of FIG. 1 . Each DST executionunit includes the DST client module 34 of FIG. 1 and the memory 88 ofFIG. 3 . Each DST execution unit may be implemented utilizing the DSTexecution unit 36 of FIG. 1 . Alternatively, any DST execution unit maybe utilized to perform functions described below with regards to the DSTintegrity processing unit 20.

The DSN functions to select an encoded data slice rebuilding resourcewhen detecting a storage error (e.g., missing slice, corrupted slice)associated with an encoded data slice of a set of encoded data slicesstored in the set of DST execution units. In an example of operation ofthe selecting of the encoded data slice rebuilding resource, the DSTintegrity processing unit 20 detects a storage error associated with theencoded data slice of the set of encoded data slices, where a datasegment was dispersed storage error encoded to produce the set ofencoded data slices. The data segment is associated with a revisionlevel and the set of encoded data slices are associated with therevision level. The set of encoded data slices are further associatedwith a set of corresponding slice names and a corresponding slice nameof the set of slice names associated with the encoded data slice of thestorage error. The detecting includes at least one of interpreting readslice responses, interpreting list slice responses, and interpreting anerror message. For example, the DST integrity processing unit obtainsslice availability information 640 (e.g., a slice name, a revisionlevel, and availability level) from each of the DST execution units withregards to available encoded data slices of each of the sets of encodeddata slices stored in the set of DST execution units. For instance, theDST integrity processing unit 20 detects storage errors associated withencoded data slices A-2-R2 and A-4-R2 corresponding to a set of encodeddata slices of a second revision of a data segment A.

Having detected the storage error, the DST integrity processing unit 20identifies available encoded data slices of the set of encoded dataslices as candidate encoded data slices to be utilized for a rebuildingprocess to produce a rebuilt encoded data slice for the encoded dataslice of the storage error. The identifying includes at least one ofinterpreting read slice responses, interpreting list slice responses,and identifying available slices of the revision level from theresponses. For example, the DST integrity processing unit identifiesencoded data slices A-1-R2, A-3-R2, and others of the set of encodeddata slices associated with the second revision of the data segment A.

Having identified the available encoded data slices of the set ofencoded data slices as candidate encoded data slices, the DST integrityprocessing unit 20 identifies DST execution units associated with theidentified available encoded data slices of the revision level. Forexample, the DST integrity processing unit 20 identifies DST executionunits 1, 3, and others.

Having identified the DST execution units associated with the identifiedavailable encoded data slices, the DST integrity processing unit 20selects a DST execution unit of the set of DST execution units toperform the rebuilding process to produce a rebuilding unit. Forexample, the DST integrity processing unit 20 selects a DST executionunit that includes an encoded data slice of the revision required forthe rebuilding. For instance, the DST integrity processing unit 20selects the DST execution unit 1 when the encoded data slice A-1-R2 isavailable and may be utilized in the rebuilding process.

Having selected the rebuilding unit, the DST integrity processing unit20 issues via the network 24, a rebuild request 642 to the rebuildingunit to initiate the rebuilding process. For example, the DST integrityprocessing unit 20 generates the rebuild request 642 to include one ormore of the slice name of the encoded data slice to be rebuilt, therevision level, the identified available encoded data slices for therebuilding process, identifiers of the identified DST execution unitsassociated with the identified available encoded data slices. Havinggenerated the rebuild request, the DST integrity processing unit sends,via the network 24, the rebuild request to the DST execution unit 1.

Having received the rebuild request 642, the rebuilding unit obtains adecode threshold number of encoded data slices of the set of encodeddata slices that includes the encoded data slice of the storage error,where at least one encoded data slice includes a locally retrievedencoded data slice, dispersed storage error decodes the obtained decodethreshold number of encoded data slices to reproduce the data segment,dispersed storage error encodes the reproduced data segment to producethe rebuilt encoded data slice, and facilitate storage of the rebuiltencoded data slice (e.g., sends, via the network 24, the rebuilt encodeddata slice to a DST execution unit corresponding to the encoded dataslice of the storage error).

FIG. 48B is a flowchart illustrating an example of selecting an encodeddata slice rebuilding resource. The method includes step 650 where aprocessing module (e.g., of a distributed storage and task (DST) clientmodule) detects a storage error associated with an encoded data slice.The detecting includes at least one of interpreting an error message,interpreting read slice responses, and interpreting a list sliceresponses. The method continues at step 652 where the processing moduleidentifies available encoded data slices of a set of encoded data slicesthat includes encoded data slice. The identifying includes at least oneof interpreting read slice responses and interpreting list sliceresponses.

The method continues at step 654 where the processing module identifiesstorage units associated with the available encoded data slices. Theidentifying includes at least one of interpreting a dispersed storagenetwork (DSN) directory, interpreting an index, and interpreting anencoded data slice location table. The method continues at step 656where the processing module selects a storage unit of the identifiedstorage units to perform a rebuilding process. For example, theprocessing module identifies a storage unit associated with storage ofat least one of the available encoded data slices of the set of encodeddata slices (e.g., a slice name and revision level matches).

The method continues at step 658 where the processing module issues arebuild request to the selected storage unit. The issuing includes theprocessing module generating the rebuild request to include one or moreof identifiers of the identified available encoded data slices,identifiers of the identified storage units, a slice name associatedwith encoded data slice to be rebuilt, and a revision level. The issuingfurther includes sending the rebuild request to the selected storageunit.

The method continues at step 660 where the selected storage unitinitiates the rebuilding process based on the rebuild request. Theinitiating includes one or more of obtaining representations of at leasta decode threshold number of encoded data slices of the set of encodeddata slices from at least some of the identified storage units,dispersed storage error decoding the obtained representations of thedecode threshold number of encoded data slices to reproduce a datasegment, dispersed storage error decoding the reproduced data segment toproduce the rebuilt encoded data slice, and facilitate storage of therebuilt encoded data slice (e.g., sending the rebuilt encoded data sliceto another storage unit of the set of storage units, where the otherstorage unit is associated with storage of the encoded data slice).

FIG. 49A is a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes a set of distributedstorage and task (DST) execution units 1-n, the network 24 of FIG. 1 ,and the distributed storage and task network (DSTN) managing unit 18 ofFIG. 1 . Each DST execution unit is associated with an access sensor670, the processing module 84 of FIG. 3 , and the memory 88 of FIG. 3 .The access sensor 670 may be implemented within the DST execution unitin a first implementation instance or external to the DST execution unitbut within proximity (e.g., within a same room) in a second implicationinstance. The access sensor 670 includes one or more of an access panelswitch, a vibration detector, a light sensor, a humidity sensor, atemperature sensor, a barometric pressure sensor, a radioactivitysensor, a static electricity sensor, a lightning detector sensor, animage sensor, a pattern matching detector, a fingerprint reader, amotion sensor, and a DNA sensor.

The DSN functions to diminish an unfavorable impact of an unauthorizedaccess to a storage resource of the DSN. In an example of operation ofthe diminishing of the unfavorable impact, the processing module 84 of aDST execution unit detects a potentially unauthorized access to the DSTexecution unit. The detecting includes at least one of interpreting oneor more access sensors 670 to produce an access indication, detecting apredetermined pattern of a plurality of access sensor outputs,interpreting an error message, and detecting an unfavorable comparisonof a current timestamp to a schedule of authorized access. For example,the processing module 84 of the DST execution unit 1 detects opening ofan access panel of the DST execution unit by interpreting an accesspanel switch access sensor.

Having detected the potentially unauthorized access to the DST executionunit, the processing module 84 issues access information 672 to one ormore of the DST managing unit 18 and one or more other DST executionunits of the set of DST execution units. The issuing includes generatingthe access information 672 to include one or more of an identifier ofthe DST execution unit, an identifier of the access sensor, a value ofthe access sensor, an access level (e.g., quantified access data), atimestamp of the detection, and raw sensor data from one or more otheraccess sensors (e.g., a video clip leading up to the detection of theunauthorized access, an audio recording).

Having issued the access information 672, the processing module 84initiates a primary unauthorized access abatement process. Theinitiating includes performing one or more of deleting operationalinformation and/or encoded data slices including a local sliceencryption key, deleting a local authentication key, deleting the localsecurity credential, deleting an encoded data slice in accordance with apredetermined data deletion approach upon unauthorized access, deletingall encoded data slices, initiating a slice integrity testing process,temporarily disabling network access with regards to accessing encodeddata slices, and prioritizing migration of one or more encoded dataslices to another DST execution unit in accordance with a predeterminedmigration approach.

When receiving the access information 672, another DST execution unitinitiates a secondary unauthorized access abatement process. Thesecondary unauthorized access abatement process includes performing oneor more other DST execution unit defensive processes including updatingan encryption key, updating and authorization key, updating securitycredentials, initiating slice integrity testing, and restricting encodeddata slice access (e.g., to a predetermined list of requestingentities).

FIG. 49B is a flowchart illustrating an example of diminishing anunfavorable impact of an unauthorized access to a storage resource. Themethod includes step 676 where a processing module (e.g., of adistributed storage and task (DST) client module of a storage unit)detects a potentially unauthorized access to the storage unit of a setof storage units. The detecting includes interpreting one or more accesssensors to produce an access indication, comparing a current timestampand/or access type to an authorized access schedule, and indicating thepotentially unauthorized access when the comparison is unfavorable.

The method continues at step 678 where the processing module issuesaccess information to at least some of the remaining storage units ofthe set of storage units, where the access information is with regardsto the detected potentially unauthorized access. For example, theprocessing module generates the access information and sends thegenerated access information to the at least some of the remainingstorage units of the set of storage units and/or to a managing unit.

The method continues at step 680 where the processing module initiatesan unauthorized access abatement process for the storage unit. Theinitiating includes performing the unauthorized access abatement processfor the storage unit of the detected potentially unauthorized access.When receiving the access information, the method continues at step 682where another storage unit of the set of storage units initiates a localunauthorized access abatement process. The initiating includesperforming the local unauthorized access abatement process for the otherstorage unit with regards to the detected potentially unauthorizedaccess of the storage unit.

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “operably coupled to”, “coupled to”, and/or “coupling” includesdirect coupling between items and/or indirect coupling between items viaan intervening item (e.g., an item includes, but is not limited to, acomponent, an element, a circuit, and/or a module) where, for indirectcoupling, the intervening item does not modify the information of asignal but may adjust its current level, voltage level, and/or powerlevel. As may further be used herein, inferred coupling (i.e., where oneelement is coupled to another element by inference) includes direct andindirect coupling between two items in the same manner as “coupled to”.As may even further be used herein, the term “operable to” or “operablycoupled to” indicates that an item includes one or more of powerconnections, input(s), output(s), etc., to perform, when activated, oneor more its corresponding functions and may further include inferredcoupling to one or more other items. As may still further be usedherein, the term “associated with”, includes direct and/or indirectcoupling of separate items and/or one item being embedded within anotheritem. As may be used herein, the term “compares favorably”, indicatesthat a comparison between two or more items, signals, etc., provides adesired relationship. For example, when the desired relationship is thatsignal 1 has a greater magnitude than signal 2, a favorable comparisonmay be achieved when the magnitude of signal 1 is greater than that ofsignal 2 or when the magnitude of signal 2 is less than that of signal1.

As may also be used herein, the terms “processing module”, “processingcircuit”, and/or “processing unit” may be a single processing device ora plurality of processing devices. Such a processing device may be amicroprocessor, micro-controller, digital signal processor,microcomputer, central processing unit, field programmable gate array,programmable logic device, state machine, logic circuitry, analogcircuitry, digital circuitry, and/or any device that manipulates signals(analog and/or digital) based on hard coding of the circuitry and/oroperational instructions. The processing module, module, processingcircuit, and/or processing unit may be, or further include, memoryand/or an integrated memory element, which may be a single memorydevice, a plurality of memory devices, and/or embedded circuitry ofanother processing module, module, processing circuit, and/or processingunit. Such a memory device may be a read-only memory, random accessmemory, volatile memory, non-volatile memory, static memory, dynamicmemory, flash memory, cache memory, and/or any device that storesdigital information. Note that if the processing module, module,processing circuit, and/or processing unit includes more than oneprocessing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

The present invention has been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claimed invention. Further, theboundaries of these functional building blocks have been arbitrarilydefined for convenience of description. Alternate boundaries could bedefined as long as the certain significant functions are appropriatelyperformed. Similarly, flow diagram blocks may also have been arbitrarilydefined herein to illustrate certain significant functionality. To theextent used, the flow diagram block boundaries and sequence could havebeen defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claimed invention. One of average skill in the artwill also recognize that the functional building blocks, and otherillustrative blocks, modules and components herein, can be implementedas illustrated or by discrete components, application specificintegrated circuits, processors executing appropriate software and thelike or any combination thereof.

The present invention may have also been described, at least in part, interms of one or more embodiments. An embodiment of the present inventionis used herein to illustrate the present invention, an aspect thereof, afeature thereof, a concept thereof, and/or an example thereof. Aphysical embodiment of an apparatus, an article of manufacture, amachine, and/or of a process that embodies the present invention mayinclude one or more of the aspects, features, concepts, examples, etc.described with reference to one or more of the embodiments discussedherein. Further, from figure to figure, the embodiments may incorporatethe same or similarly named functions, steps, modules, etc. that may usethe same or different reference numbers and, as such, the functions,steps, modules, etc. may be the same or similar functions, steps,modules, etc. or different ones.

While the transistors in the above described figure(s) is/are shown asfield effect transistors (FETs), as one of ordinary skill in the artwill appreciate, the transistors may be implemented using any type oftransistor structure including, but not limited to, bipolar, metal oxidesemiconductor field effect transistors (MOSFET), N-well transistors,P-well transistors, enhancement mode, depletion mode, and zero voltagethreshold (VT) transistors.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of the various embodimentsof the present invention. A module includes a processing module, afunctional block, hardware, and/or software stored on memory forperforming one or more functions as may be described herein. Note that,if the module is implemented via hardware, the hardware may operateindependently and/or in conjunction software and/or firmware. As usedherein, a module may contain one or more sub-modules, each of which maybe one or more modules.

While particular combinations of various functions and features of thepresent invention have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent invention is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by a storage network, themethod comprises: maintaining loading and data access rate informationfor a solid-state storage node of the storage network; estimating afuture data access rate for the solid-state storage node; determining aprobability level of potential future data loss, based on the estimatedfuture data access rate; and in response to a determination that theprobability level of potential future data loss compares unfavorably toa maximum probability of data loss threshold level, facilitatingmigration of at least a portion of data stored on the solid statestorage node for temporary storage in another storage node of thestorage network.
 2. The method of claim 1, further comprising:suspending, upon the probability level of potential future data losscomparing unfavorably to a maximum probability of data loss thresholdlevel, the execution of one or more ongoing data access tasks for thestorage node.
 3. The method of claim 2, wherein the data is stored assets of encoded data slices and the suspending includes one or more of:storing fewer slices of each set of newly stored encoded data slices, orprioritizing data access tasks.
 4. The method of claim 1, wherein theestimated future data access rate is based on write data access andmaintenance tasks associated with the s storage node.
 5. The method ofclaim 1, wherein the estimated future data access rate is based on writedata access and an elapsed time for data stored in the storage node. 6.The method of claim 1, wherein the estimated future data access rate isgenerated based on one or more of: storage node loading information,interpreting a task queue, interpreting current loading rates,interpreting a schedule, or interpreting a message.
 7. The method ofclaim 1, wherein the determining includes at least one of: estimating afuture capacity for execution of maintenance tasks or estimating a dataretrieval reliability level.
 8. The method of claim 1, wherein the datais stored as sets of encoded data slices and the facilitating includesone or more of: storing additional encoded data slices of each set ofnewly stored encoded data slices, prioritizing maintenance tasks, or ade-prioritizing data access tasks.
 9. The method of claim 8, wherein thestoring additional slices of each set of newly stored encoded dataslices includes increasing a write threshold number.
 10. The method ofclaim 8, wherein the storing additional slices of each set of newlystored encoded data slices includes decreasing a read threshold number.11. A computing device of a storage network, the computing devicecomprises: an interface; a local memory; and a processing moduleoperably coupled to the interface and the local memory, wherein theprocessing module functions to: maintain loading and data access rateinformation for a solid-state storage node of the storage network;estimate a future data access rate for the solid-state storage node;determine a probability level of potential future data loss, based onthe estimated future data access rate; and in response to adetermination that the probability level of potential future data losscompares unfavorably to a maximum probability of data loss thresholdlevel, suspend, upon a current data access task rate being greater thana maximum task rate level, facilitate execution of a preventative dataloss mitigation process.
 12. The computing device of claim 11, whereinthe data is stored as sets of encoded data slices and the suspendingincludes one or more of: storing fewer slices of each set of newlystored encoded data slices, or prioritizing data access tasks.
 13. Thecomputing device of claim 11, wherein the estimated future data accessrate is based on write data access and maintenance tasks associated withthe storage node.
 14. The computing device of claim 11, wherein theestimated future data access rate is based on write data access and anelapsed time for data stored in the storage node.
 15. The computingdevice of claim 11, wherein the estimated future data access rate isgenerated based on one or more of: storage node loading information,interpreting a task queue, interpreting current loading rates,interpreting a schedule, or interpreting a message.
 16. The computingdevice of claim 11, wherein the determining includes at least one of:estimating a future capacity for execution of maintenance tasks orestimating a data retrieval reliability level.
 17. The computing deviceof claim 11, wherein the data is stored as sets of encoded data slicesand the facilitating includes one or more of: storing additional encodeddata slices of each set of newly stored encoded data slices,prioritizing maintenance tasks, or a de-prioritizing data access tasks.18. The computing device of claim 17, wherein the processing modulefunctions to store additional slices of each set of newly stored encodeddata slices by increasing a write threshold number.
 19. The computingdevice of claim 18, wherein the processing module functions to storeadditional slices of each set of newly stored encoded data slices bydecreasing a read threshold number.
 20. A system comprises: aninterface; a local memory; and a processing module operably coupled tothe interface and the local memory, wherein the processing modulefunctions to: maintain loading and data access rate information for asolid-state storage node of a storage network, wherein data is stored assets of encoded data slices in the storage node; estimate a future dataaccess rate for the solid-state storage node; determine a probabilitylevel of potential future data loss, based on the estimated future dataaccess rate; and in response to a determination that the probabilitylevel of potential future data loss compares unfavorably to a maximumprobability of data loss threshold level, store additional encoded dataslices of each set of any encoded data slices for which a write accesstask is received by the storage node.